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: "Two-stage sparse representation for robust recognition on large-scale database"
  Abstract: This paper proposes a novel robust sparse representation method, called the two-stage sparse representation (TSR), for robust recognition on a large-scale database. Based on the divide and conquer strategy, TSR divides the procedure of robust recognition into outlier detection stage and recognition stage. In the first stage, a weighted linear regression is used to learn a metric in which noise and outliers in image pixels are detected. In the second stage, based on the learnt metric, the large-scale dataset is firstly filtered into a small set according to the nearest neighbor criterion. Then a sparse representation is computed by the non-negative least squares technique. The sparse solution is unique and can be optimized efficiently. The extensive numerical experiments on several public databases demonstrate that the proposed TSR approach generally obtains better classification accuracy than the state-of-the-art Sparse Representation Classification (SRC). At the same time, by using the TSR, a significant reduction of computational cost is reached by over fifty times in comparison with the SRC, which enables the TSR to be deployed more suitably for large-scale dataset.

Connected through hyperedge HE-1 (induced by source paper "Sparse Representation for Computer Vision and Pattern Recognition"):
  with members:
    - "Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries"
    - "Robust object tracking via sparsity-based collaborative model"
    - "Multiple Kernel Learning for Hyperspectral Image Classification: A Review"
    - "Visual Object Tracking Using Structured Sparse PCA-Based Appearance Representation and Online Learning"
    - "A Probabilistic Framework for Discriminative Dictionary Learning"
    - "Accelerometer-based gesture recognition using dynamic time warping and sparse representation"

Connected through hyperedge HE-2 (induced by source paper "Hyperspectral Data Dimensionality Reduction Based on Non-negative Sparse Semi-supervised Framework"):
  with members:
    (none)

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