human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups authors who co-authored the same paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "An Integrated Model for Bayesian Learning of Sparse Representation and Classifier Training")
  Member "A Decomposition Framework for Image Denoising Algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "A framework for objective image quality measures based on intuitionistic fuzzy sets")

Question: Please tell me which research area the co-authored paper (i.e., the source paper of the center hyperedge) belongs to. The 4 classes are: Computer science, Mathematics, Engineering, Economics, Psychology, Business. Directly output the class name.

gpt: Mathematics