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: "Dense Auto-Encoder Hashing for Robust Cross-Modality Retrieval"
  Abstract: Cross-modality retrieval has been widely studied, which aims to search images as response to text queries or vice versa. When faced with large-scale dataset, cross-modality hashing serves as an efficient and effective solution, which learns binary codes to approximate the cross-modality similarity in the Hamming space. Most recent cross-modality hashing schemes focus on learning the hash functions from data instances with fully modalities. However, how to learn robust binary codes when facing incomplete modality (i.e., with one modality missed or partially observed), is left unexploited, which however widely occurs in real-world applications. In this paper, we propose a novel cross-modality hashing, termed Dense Auto-encoder Hashing (DAH), which can explicitly impute the missed modality and produce robust binary codes by leveraging the relatedness among different modalities. To that effect, we propose a novel Dense Auto-encoder Network (DAN) to impute the missing modalities, which densely connects each layer to every other layer in a feed-forward fashion. For each layer, a noisy auto-encoder block is designed to calculate the residue between the current prediction and original data. Finally, a hash-layer is added to the end of DAN, which serves as a special binary encoder model to deal with the incomplete modality input. Quantitative experiments on three cross-modality visual search benchmarks,  i.e.,  the Wiki, NUS-WIDE, and FLICKR-25K, have shown that the proposed DAH has superior performance over the state-of-the-art approaches.

Connected through hyperedge HE-1 (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"
    - "Compact Image Fingerprint Via Multiple Kernel Hashing"

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