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 "Compact Image Fingerprint Via Multiple Kernel Hashing"):
  Abstract: Image fingerprinting is regarded as an alternative approach to watermarking in terms of near-duplicate detection application. It consists of feature extraction and feature indexing. Generally, the former is mainly related to discrimination, robustness , and security while the latter closely focuses on the efficiency of fingerprints search. To enable fast fingerprints searching over a very large database, we propose a new kernelized multiple feature hashing method to convert the real-value fingerprints into compact binary-value fingerprints. During the process of converting, the proposed hashing method jointly utilizes the kernel trick and multiple feature fusion strategy to map the image represented by multiple features into a compact binary code. With the help of the kernel function, the hashing method can be applied to any format (such as string, graph, set, and so on) as long as there is an associated kernel function available for similarity measurement. In addition, taking multiple features into account aims at improving the discriminability since these multiple evidences are complementary to each other. The extensive experimental results show that the proposed algorithm outperforms state-of-the-art kernelized hashing methods by up to 10 percent.

Includes members:
  Member "Iterative Quantization: A Procrustean Approach to Learning Binary Codes for Large-Scale Image Retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Building Rome on a cloudless day")
  Member "Complementary hashing for approximate nearest neighbor search":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Entropy based nearest neighbor search in high dimensions")
  Member "Robustness and Discrimination Oriented Hashing Combining Texture and Invariant Vector Distance":
    also connected through other hyperedges:
      (none)

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