human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups authors who co-authored the same paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "Two-stage sparse representation for robust recognition on large-scale database":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Sparse Representation for Computer Vision and Pattern Recognition")

Question: Please tell me which research area the co-authored paper (i.e., the source paper of the center hyperedge) belongs to. The 4 classes are: Computer science, Mathematics, Engineering, Economics, Psychology, Business. Directly output the class name.

gpt: Computer science