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: "Complementary hashing for approximate nearest neighbor search"
  Abstract: Recently, hashing based Approximate Nearest Neighbor (ANN) techniques have been attracting lots of attention in computer vision. The data-dependent hashing methods, e.g., Spectral Hashing, expects better performance than the data-blind counterparts, e.g., Locality Sensitive Hashing (LSH). However, most data-dependent hashing methods only employ a single hash table. When higher recall is desired, they have to retrieve exponentially growing number of hash buckets around the bucket containing the query, which may drag down the precision rapidly. In this paper, we propose a so-called complementary hashing approach, which is able to balance the precision and recall in a more effective way. The key idea is to employ multiple complementary hash tables, which are learned sequentially in a boosting manner, so that, given a query, its true nearest neighbors missed from the active bucket of one hash table are more likely to be found in the active bucket of the next hash table. Compared with LSH that also can exploit multiple hash tables, our approach is more effective to find true NNs, thanks to the complementarity property of the hash tables from our approach. Experimental results on large scale ANN search show that the proposed method significantly improves the performance and outperforms the state-of-the-art.

Connected through hyperedge HE-1 (induced by source paper "Compact Image Fingerprint Via Multiple Kernel Hashing"):
  with members:
    - "Iterative Quantization: A Procrustean Approach to Learning Binary Codes for Large-Scale Image Retrieval"
    - "Robustness and Discrimination Oriented Hashing Combining Texture and Invariant Vector Distance"

Connected through hyperedge HE-2 (induced by source paper "Entropy based nearest neighbor search in high dimensions"):
  with members:
    - "Security analysis of cryptographically controlled access to XML documents"
    - "SIMP: accurate and efficient near neighbor search in high dimensional spaces"
    - "Fast and Accurate k-means For Large Datasets"

Connected through hyperedge HE-3 (induced by source paper "Supervised hashing with kernels"):
  with members:
    - "Iterative Quantization: A Procrustean Approach to Learning Binary Codes for Large-Scale Image Retrieval"
    - "Correlation Autoencoder Hashing for Supervised Cross-Modal Search"

Connected through hyperedge HE-4 (induced by source paper "Query-driven iterated neighborhood graph search for large scale indexing"):
  with members:
    - "Scalable Person Re-identification: A Benchmark"

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