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: "A Scale Adaptive Kernel Correlation Filter Tracker with Feature Integration"
  Abstract: Although the correlation filter-based trackers achieve the competitive results both on accuracy and robustness, there is still a need to improve the overall tracking capability. In this paper, we presented a very appealing tracker based on the correlation filter framework. To tackle the problem of the fixed template size in kernel correlation filter tracker, we suggest an effective scale adaptive scheme. Moreover, the powerful features including HoG and color-naming are integrated together to further boost the overall tracking performance. The extensive empirical evaluations on the benchmark videos and VOT 2014 dataset demonstrate that the proposed tracker is very promising for the various challenging scenarios. Our method successfully tracked the targets in about 72% videos and outperformed the state-of-the-art trackers on the benchmark dataset with 51 sequences.

Connected through hyperedge HE-1 (induced by source paper "Online Object Tracking: A Benchmark"):
  with members:
    - "Robust object tracking via sparsity-based collaborative model"
    - "Object Tracking Benchmark"
    - "Multi attention module for visual tracking"
    - "Clustering Local Motion Estimates for Robust and Efficient Object Tracking"

Connected through hyperedge HE-2 (induced by source paper "Robust object tracking via sparsity-based collaborative model"):
  with members:
    - "Sparse Representation for Computer Vision and Pattern Recognition"
    - "Online Object Tracking: A Benchmark"

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