human: Given a node-centered hypergraph: <hypergraph>, where nodes represent authors and hyperedges represent groups of authors who co-authored the same paper.

Details of the hypergraph:
Center node: "Hyperspectral Data Dimensionality Reduction Based on Non-negative Sparse Semi-supervised Framework"
  Abstract: A non-negative sparse semi-supervised dimensionality reduction framework is proposed for hyperspectral data. The framework consists of two parts: 1) a discriminant item is designed to analyze the few labeled samples from the global viewpoint, which can assess the separability between each surface object; 2) a regularization term is used to build a non-negative sparse representation graph based on large scale unlabelled samples, which can adaptively find an adjacency graph for each sample and then find valuable samples from the original hyperspectral data. Based on the framework and the maximum margin criterion, a dimensionality reduction algorithm called non-negative sparse semi-supervised maximum margin criterion is proposed. Experimental results on the AVIRIS 92AV3C hyperspectral data show that the proposed algorithm can effectively utilize the unlabelled samples to obtain higher overall classification accuracy.

Connected through hyperedge HE-1 (induced by source paper "Two-stage sparse representation for robust recognition on large-scale database"):
  with members:
    - "Sparse Representation for Computer Vision and Pattern Recognition"

Question: Please tell me which research area the center author belongs to. The 4 classes are: Computer science, Mathematics, Engineering, Economics, Psychology, Business. Directly output the class name.

gpt: Computer science