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: "Image Deblurring and Super-Resolution by Adaptive Sparse Domain Selection and Adaptive Regularization"
  Abstract: As a powerful statistical image modeling technique, sparse representation has been successfully used in various image restoration applications. The success of sparse representation owes to the development of the  l  1 -norm optimization techniques and the fact that natural images are intrinsically sparse in some domains. The image restoration quality largely depends on whether the employed sparse domain can represent well the underlying image. Considering that the contents can vary significantly across different images or different patches in a single image, we propose to learn various sets of bases from a precollected dataset of example image patches, and then, for a given patch to be processed, one set of bases are adaptively selected to characterize the local sparse domain. We further introduce two adaptive regularization terms into the sparse representation framework. First, a set of autoregressive (AR) models are learned from the dataset of example image patches. The best fitted AR models to a given patch are adaptively selected to regularize the image local structures. Second, the image nonlocal self-similarity is introduced as another regularization term. In addition, the sparsity regularization parameter is adaptively estimated for better image restoration performance. Extensive experiments on image deblurring and super-resolution validate that by using adaptive sparse domain selection and adaptive regularization, the proposed method achieves much better results than many state-of-the-art algorithms in terms of both PSNR and visual perception.

Connected through hyperedge HE-1 (induced by source paper "Image super-resolution reconstruction based on adaptive sparse representation: Image Super-resolution Reconstruction Based on Adaptive Sparse Representation"):
  with members:
    (none)

Connected through hyperedge HE-2 (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 Denoising and Inpainting with Deep Neural Networks"
    - "An Integrated Model for Bayesian Learning of Sparse Representation and Classifier Training"

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: Mathematics