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Cognitive diagnosis is a vital upstream task in intelligent education systems. It models the student-exercise interaction, aiming to infer the students' proficiency levels on each knowledge concept. This paper observes that most existing methods can hardly effectively capture the homogeneous influence due to its inherent complexity. That is to say, although students exhibit similar performance on given exercises, their proficiency levels inferred by these methods vary significantly, resulting in shortcomings in interpretability and efficacy. Given the complexity of homogeneous influence, a hypergraph could be a choice due to its flexibility and capability of modeling high-order similarity which aligns with the nature of homogeneous influence. However, before incorporating hypergraph, one at first needs to address the challenges of distorted homogeneous influence, sparsity of response logs, and over-smoothing. To this end, this paper proposes a hypergraph cognitive diagnosis model (HyperCDM) to address these challenges and effectively capture the homogeneous influence. Specifically, to avoid distortion, HyperCDM employs a divide-and-conquer strategy to learn student, exercise and knowledge representations in their own hypergraphs respectively, and interconnects them via a feature-based interaction function. To construct hypergraphs based on sparse response logs, the auto-encoder is utilized to preprocess response logs and 𝐾-means is applied to cluster students. To mitigate over-smoothing, momentum hypergraph convolution networks are designed to partially keep previous representations during the message propagation. Extensive experiments on both offline and online real-world datasets show that HyperCDM achieves state-of-the-art performance in terms of interpretability and capturing homogeneous influence effectively, and is competitive in generalization. The ablation study verifies the efficacy of each component, and the case study explicitly showcases the homogeneous influence captured by HyperCDM.