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 Denoising in Mixed Poisson–Gaussian Noise"
  Abstract: We propose a general methodology (PURE-LET) to design and optimize a wide class of transform-domain thresholding algorithms for denoising images corrupted by mixed Poisson-Gaussian noise. We express the denoising process as a linear expansion of thresholds (LET) that we optimize by relying on a purely data-adaptive unbiased estimate of the mean-squared error (MSE), derived in a non-Bayesian framework (PURE: Poisson-Gaussian unbiased risk estimate). We provide a practical approximation of this theoretical MSE estimate for the tractable optimization of arbitrary transform-domain thresholding. We then propose a pointwise estimator for undecimated filterbank transforms, which consists of subband-adaptive thresholding functions with signal-dependent thresholds that are globally optimized in the image domain. We finally demonstrate the potential of the proposed approach through extensive comparisons with state-of-the-art techniques that are specifically tailored to the estimation of Poisson intensities. We also present denoising results obtained on real images of low-count fluorescence microscopy.

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 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 "Colocalization Estimation Using Graphical Modeling and Variational Bayesian Expectation Maximization: Towards a Parameter-Free Approach"):
  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: Mathematics