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: "A study of graph-based system for multi-view clustering"
  Abstract: Abstract   This paper studies clustering of multi-view data, known as multi-view clustering. Among existing multi-view clustering methods, one representative category of methods is the graph-based approach. Despite its elegant and simple formulation, the graph-based approach has not been studied in terms of (a) the generalization of the approach or (b) the impact of different graph metrics on the clustering results. This paper extends this important approach by first proposing a general  G raph- B ased  S ystem (GBS) for multi-view clustering, and then discussing and evaluating the impact of different graph metrics on the multi-view clustering performance within the proposed framework. GBS works by extracting data feature matrix of each view, constructing graph matrices of all views, and fusing the constructed graph matrices to generate a unified graph matrix, which gives the final clusters. A novel multi-view clustering method that works in the GBS framework is also proposed, which can (1) construct data graph matrices effectively, (2) weight each graph matrix automatically, and (3) produce clustering results directly. Experimental results on benchmark datasets show that the proposed method outperforms state-of-the-art baselines significantly.

Connected through hyperedge HE-1 (induced by source paper "Unified subspace learning for incomplete and unlabeled multi-view data"):
  with members:
    - "A Secure and Dynamic Multi-Keyword Ranked Search Scheme over Encrypted Cloud Data"

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