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: "Query-driven iterated neighborhood graph search for large scale indexing"
  Abstract: In this paper, we address the approximate nearest neighbor (ANN) search problem over large scale visual descriptors. We investigate a simple but very effective approach, neighborhood graph search, which constructs a neighborhood graph to index the data points and conducts a local search, expanding neighborhoods with a best-first manner, for ANN search. Our empirical analysis shows that neighborhood expansion is very efficient, with  O(1)  cost, for a new NN candidate location, and has high chances to locate true NNs and hence it usually performs well. However, it often gets sub-optimal solutions since local search only checks the neighborhood of the current solution, or conducts exhaustive and continuous neighborhood expansions to find better solutions, which deteriorates the query efficiency.   In this paper, we propose a query-driven iterated neighborhood graph search approach to improve the performance. We follow the iterated local search (ILS) strategy, widely-used in combinatorial optimization, to find a solution beyond a local optimum. We handle the key challenge in making neighborhood graph search adapt to ILS, Perturbation, which generates a new pivot to restart a local search. To this end, we present a criterion to check if the local search over a neighborhood graph arrives at the local solution. Moreover, we exploit the query and search history to design the perturbation scheme, resulting in a more effective search. The major benefit is avoiding unnecessary neighborhood expansions and hence more efficiently finding true NNs. Experimental results on large scale SIFT matching, similar image search, and shape retrieval with non-metric distance measures, show that our approach performs much better than previous state-of-the-art ANN search approaches.

Connected through hyperedge HE-1 (induced by source paper "Scalable Person Re-identification: A Benchmark"):
  with members:
    - "Online Inter-Camera Trajectory Association Exploiting Person Re-Identification and Camera Topology"
    - "SKEPRID: Pose and Illumination Change-Resistant Skeleton-Based Person Re-Identification"

Connected through hyperedge HE-2 (induced by source paper "Complementary hashing for approximate nearest neighbor search"):
  with members:
    - "Supervised hashing with kernels"
    - "Compact Image Fingerprint Via Multiple Kernel Hashing"
    - "Entropy based nearest neighbor search in high dimensions"

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