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: "Object Tracking Benchmark"
  Abstract: Object tracking has been one of the most important and active research areas in the field of computer vision. A large number of tracking algorithms have been proposed in recent years with demonstrated success. However, the set of sequences used for evaluation is often not sufficient or is sometimes biased for certain types of algorithms. Many datasets do not have common ground-truth object positions or extents, and this makes comparisons among the reported quantitative results difficult. In addition, the initial conditions or parameters of the evaluated tracking algorithms are not the same, and thus, the quantitative results reported in literature are incomparable or sometimes contradictory. To address these issues, we carry out an extensive evaluation of the state-of-the-art online object-tracking algorithms with various evaluation criteria to understand how these methods perform within the same framework. In this work, we first construct a large dataset with ground-truth object positions and extents for tracking and introduce the sequence attributes for the performance analysis. Second, we integrate most of the publicly available trackers into one code library with uniform input and output formats to facilitate large-scale performance evaluation. Third, we extensively evaluate the performance of 31 algorithms on 100 sequences with different initialization settings. By analyzing the quantitative results, we identify effective approaches for robust tracking and provide potential future research directions in this field.

Connected through hyperedge HE-1 (induced by source paper "Multi attention module for visual tracking"):
  with members:
    - "Online Object Tracking: A Benchmark"

Connected through hyperedge HE-2 (induced by source paper "Visual Object Tracking Using Structured Sparse PCA-Based Appearance Representation and Online Learning"):
  with members:
    - "Sparse Representation for Computer Vision and Pattern Recognition"

Connected through hyperedge HE-3 (induced by source paper "Salient Superpixel Visual Tracking with Coarse-to-Fine Segmentation and Manifold Ranking"):
  with members:
    (none)

Connected through hyperedge HE-4 (induced by source paper "Online Object Tracking: A Benchmark"):
  with members:
    - "Robust object tracking via sparsity-based collaborative model"
    - "A Scale Adaptive Kernel Correlation Filter Tracker with Feature Integration"
    - "Multi attention module for visual tracking"
    - "Clustering Local Motion Estimates for Robust and Efficient Object Tracking"

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