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: "Iterative Quantization: A Procrustean Approach to Learning Binary Codes for Large-Scale Image Retrieval"
  Abstract: This paper addresses the problem of learning similarity-preserving binary codes for efficient similarity search in large-scale image collections. We formulate this problem in terms of finding a rotation of zero-centered data so as to minimize the quantization error of mapping this data to the vertices of a zero-centered binary hypercube, and propose a simple and efficient alternating minimization algorithm to accomplish this task. This algorithm, dubbed iterative quantization (ITQ), has connections to multiclass spectral clustering and to the orthogonal Procrustes problem, and it can be used both with unsupervised data embeddings such as PCA and supervised embeddings such as canonical correlation analysis (CCA). The resulting binary codes significantly outperform several other state-of-the-art methods. We also show that further performance improvements can result from transforming the data with a nonlinear kernel mapping prior to PCA or CCA. Finally, we demonstrate an application of ITQ to learning binary attributes or "classemes" on the ImageNet data set.

Connected through hyperedge HE-1 (induced by source paper "Compact Image Fingerprint Via Multiple Kernel Hashing"):
  with members:
    - "Complementary hashing for approximate nearest neighbor search"
    - "Robustness and Discrimination Oriented Hashing Combining Texture and Invariant Vector Distance"

Connected through hyperedge HE-2 (induced by source paper "Building Rome on a cloudless day"):
  with members:
    - "Accurate, Dense, and Robust Multiview Stereopsis"
    - "Technical Section: Automatic urban modeling using volumetric reconstruction with surface graph cuts"
    - "Divide and Conquer: Efficient Large-Scale Structure from Motion Using Graph Partitioning"

Connected through hyperedge HE-3 (induced by source paper "Dense Auto-Encoder Hashing for Robust Cross-Modality Retrieval"):
  with members:
    (none)

Connected through hyperedge HE-4 (induced by source paper "Supervised hashing with kernels"):
  with members:
    - "Complementary hashing for approximate nearest neighbor search"
    - "Correlation Autoencoder Hashing for Supervised Cross-Modal Search"

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