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: "Expanding diagnostically labeled datasets using content-based image retrieval"
  Abstract: In computer-aided diagnosis (CAD), having an accurate ground truth is critical. However, the number of databases containing medical images with diagnostic information is limited. Using pulmonary computed tomography (CT) scans, we develop a content-based image retrieval (CBIR) approach to exploit the limited images with diagnostically labeled data in order to annotate unlabeled images with diagnoses. By applying this CBIR method iteratively, we expand the set of diagnosed data available for CAD systems. We evaluate the method by implementing a CAD system that uses undiagnosed lung nodules as queries and retrieves similar nodules from the diagnostically labeled dataset. In calculating the precision of this system, radiologist- and computer-predicted malignancy data are used as ground truth for the undiagnosed query nodules. Our results indicate that CBIR expansion is an effective method for labeling undiagnosed images in order to improve the performance of CAD systems.

Connected through hyperedge HE-1 (induced by source paper "Putting the Content Into Context: Features and Gaps in Image Retrieval"):
  with members:
    - "Medical image retrieval and automated annotation: OHSU at ImageCLEF 2006"

Connected through hyperedge HE-2 (induced by source paper "eFis: A Fuzzy Inference Method for Predicting Malignancy of Small Pulmonary Nodules"):
  with members:
    (none)

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