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 "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.

Includes members:
  Member "Supervised hashing with kernels":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Correlation Autoencoder Hashing for Supervised Cross-Modal Search")
  Member "Compact Image Fingerprint Via Multiple Kernel Hashing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Robustness and Discrimination Oriented Hashing Combining Texture and Invariant Vector Distance")
  Member "Entropy based nearest neighbor search in high dimensions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Security analysis of cryptographically controlled access to XML documents")
  Member "Query-driven iterated neighborhood graph search for large scale indexing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "Scalable Person Re-identification: A Benchmark")

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