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: "An Integrated Model for Bayesian Learning of Sparse Representation and Classifier Training"
  Abstract: We present an integrated model for Bayesian learning of sparse representation and classifier training, and apply it for the task of visual recognition. Most previous work learns the sparse representation and trains the classifier on top of it in two separate steps. We cast these two into a unified probabilistic model. This way, the supervised labels can effectively affect the learning of the sparse representation. In the training phase, the inference of the joint expectation for dictionary, code, classifier and other variables under the observation of descriptors and labels is carried out by Gibbs Sampling. In the testing phase, based on the learned parameters, the sparse code and the class label of the image are obtained by Bayesian inference. The proposed model is evaluated on Caltech 101 dataset and its efficacy is demonstrated by a careful analysis of the experimental results.

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"
    - "A Probabilistic Framework for Discriminative Dictionary Learning"
    - "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"

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