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: "Accurate, Dense, and Robust Multiview Stereopsis"
  Abstract: This paper proposes a novel algorithm for multiview stereopsis that outputs a dense set of small rectangular patches covering the surfaces visible in the images. Stereopsis is implemented as a match, expand, and filter procedure, starting from a sparse set of matched keypoints, and repeatedly expanding these before using visibility constraints to filter away false matches. The keys to the performance of the proposed algorithm are effective techniques for enforcing local photometric consistency and global visibility constraints. Simple but effective methods are also proposed to turn the resulting patch model into a mesh which can be further refined by an algorithm that enforces both photometric consistency and regularization constraints. The proposed approach automatically detects and discards outliers and obstacles and does not require any initialization in the form of a visual hull, a bounding box, or valid depth ranges. We have tested our algorithm on various data sets including objects with fine surface details, deep concavities, and thin structures, outdoor scenes observed from a restricted set of viewpoints, and "crowded" scenes where moving obstacles appear in front of a static structure of interest. A quantitative evaluation on the Middlebury benchmark [1] shows that the proposed method outperforms all others submitted so far for four out of the six data sets.

Connected through hyperedge HE-1 (induced by source paper "Viewpoint-aware object detection and pose estimation"):
  with members:
    - "Viewpoints and keypoints"

Connected through hyperedge HE-2 (induced by source paper "Building Rome on a cloudless day"):
  with members:
    - "Iterative Quantization: A Procrustean Approach to Learning Binary Codes for Large-Scale Image Retrieval"
    - "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 "Probabilistic visibility for multi-view stereo"):
  with members:
    - "A probabilistic framework for space carving"

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