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 Probabilistic Framework for Discriminative Dictionary Learning"
  Abstract: In this paper, we address the problem of discriminative dictionary learning (DDL), where sparse linear representation and classification are combined in a probabilistic framework. As such, a single discriminative dictionary and linear binary classifiers are learned jointly. By encoding sparse representation and discriminative classification models in a MAP setting, we propose a general optimization framework that allows for a data-driven tradeoff between faithful representation and accurate classification. As opposed to previous work, our learning methodology is capable of incorporating a diverse family of classification cost functions (including those used in popular boosting methods), while avoiding the need for involved optimization techniques. We show that DDL can be solved by a sequence of updates that make use of well-known and well-studied sparse coding and dictionary learning algorithms from the literature. To validate our DDL framework, we apply it to digit classification and face recognition and test it on standard benchmarks.

Connected through hyperedge HE-1 (induced by source paper "Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries"):
  with members:
    - "Sparse Representation for Computer Vision and Pattern Recognition"
    - "Quasi Real-Time Summarization for Consumer Videos"
    - "Is Denoising Dead"
    - "Fields of Experts: a framework for learning image priors"
    - "Image Denoising in Mixed Poisson–Gaussian Noise"
    - "Image Deblurring and Super-Resolution by Adaptive Sparse Domain Selection and Adaptive Regularization"
    - "Image Denoising and Inpainting with Deep Neural Networks"
    - "An Integrated Model for Bayesian Learning of Sparse Representation and Classifier Training"

Connected through hyperedge HE-2 (induced by source paper "Sparse Representation for Computer Vision and Pattern Recognition"):
  with members:
    - "Two-stage sparse representation for robust recognition on large-scale database"
    - "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"
    - "Accelerometer-based gesture recognition using dynamic time warping and sparse representation"

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