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: "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.

Connected through hyperedge HE-1 (induced by source paper "Robustness and Discrimination Oriented Hashing Combining Texture and Invariant Vector Distance"):
  with members:
    (none)

Connected through hyperedge HE-2 (induced by source paper "Complementary hashing for approximate nearest neighbor search"):
  with members:
    - "Supervised hashing with kernels"
    - "Entropy based nearest neighbor search in high dimensions"
    - "Query-driven iterated neighborhood graph search for large scale indexing"

Connected through hyperedge HE-3 (induced by source paper "Iterative Quantization: A Procrustean Approach to Learning Binary Codes for Large-Scale Image Retrieval"):
  with members:
    - "Supervised hashing with kernels"
    - "Building Rome on a cloudless day"
    - "Dense Auto-Encoder Hashing for Robust Cross-Modality Retrieval"

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