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: "Is Denoising Dead"
  Abstract: Image denoising has been a well studied problem in the field of image processing. Yet researchers continue to focus attention on it to better the current state-of-the-art. Recently proposed methods take different approaches to the problem and yet their denoising performances are comparable. A pertinent question then to ask is whether there is a theoretical limit to denoising performance and, more importantly, are we there yet? As camera manufacturers continue to pack increasing numbers of pixels per unit area, an increase in noise sensitivity manifests itself in the form of a noisier image. We study the performance bounds for the image denoising problem. Our work in this paper estimates a lower bound on the mean squared error of the denoised result and compares the performance of current state-of-the-art denoising methods with this bound. We show that despite the phenomenal recent progress in the quality of denoising algorithms, some room for improvement still remains for a wide class of general images, and at certain signal-to-noise levels. Therefore, image denoising is not dead - yet.

Connected through hyperedge HE-1 (induced by source paper "A Decomposition Framework for Image Denoising Algorithms"):
  with members:
    - "A framework for objective image quality measures based on intuitionistic fuzzy sets"

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"
    - "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"
    - "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