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: "Real-time plane segmentation and obstacle detection of 3d point clouds for indoor scenes"
  Abstract: Scene analysis is an important issue in computer vision and extracting structural information is one of the fundamental techniques. Taking advantage of depth camera, we propose a novel fast plane segmentation algorithm and use it to detect obstacles in indoor environment. The proposed algorithm has two steps: the initial segmentation and the refined segmentation. Firstly, depth image is converted into 3D point cloud and divided into voxels, which are less sensitive to noises compared with pixels. Then area-growing algorithm is used to extract the candidate planes according to the normal of each voxel. Secondly, each point that hasn't been classified to any plane is examined whether it actually belongs to a plane. The two-step strategy has been proven to be a fast segmentation method with high accuracy. The experimental results demonstrate that our method can segment planes and detect obstacles in real-time with high accuracy for indoor scenes.

Connected through hyperedge HE-1 (induced by source paper "Mobile robot navigation and target tracking system"):
  with members:
    - "Mobile robot vision-based navigation using self-organizing and incremental neural networks"

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