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: "Viewpoint-aware object detection and pose estimation"
  Abstract: We describe an approach to category-level detection and viewpoint estimation for rigid 3D objects from single 2D images. In contrast to many existing methods, we directly integrate 3D reasoning with an appearance-based voting architecture. Our method relies on a nonparametric representation of a joint distribution of shape and appearance of the object class. Our voting method employs a novel parametrization of joint detection and viewpoint hypothesis space, allowing efficient accumulation of evidence. We combine this with a re-scoring and refinement mechanism, using an ensemble of view-specific Support Vector Machines. We evaluate the performance of our approach in detection and pose estimation of cars on a number of benchmark datasets.

Connected through hyperedge HE-1 (induced by source paper "Viewpoints and keypoints"):
  with members:
    - "Cross Modal Distillation for Supervision Transfer"

Connected through hyperedge HE-2 (induced by source paper "Accurate, Dense, and Robust Multiview Stereopsis"):
  with members:
    - "Building Rome on a cloudless day"
    - "Probabilistic visibility for multi-view stereo"

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