human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "the little engine that could regularization by denoising red"):
  Abstract: Removal of noise from an image is an extensively studied problem in image processing. Indeed, the recent advent of sophisticated and highly effective denoising algorithms lead some to believe that existing methods are touching the ceiling in terms of noise removal performance. Can we leverage this impressive achievement to treat other tasks in image processing? Recent work has answered this question positively, in the form of the Plug-and-Play Prior ($P^3$) method, showing that any inverse problem can be handled by sequentially applying image denoising steps. This relies heavily on the ADMM optimization technique in order to obtain this chained denoising interpretation. #R##N#Is this the only way in which tasks in image processing can exploit the image denoising engine? In this paper we provide an alternative, more powerful and more flexible framework for achieving the same goal. As opposed to the $P^3$ method, we offer Regularization by Denoising (RED): using the denoising engine in defining the regularization of the inverse problem. We propose an explicit image-adaptive Laplacian-based regularization functional, making the overall objective functional clearer and better defined. With a complete flexibility to choose the iterative optimization procedure for minimizing the above functional, RED is capable of incorporating any image denoising algorithm, treat general inverse problems very effectively, and is guaranteed to converge to the globally optimal result. We test this approach and demonstrate state-of-the-art results in the image deblurring and super-resolution problems.

Includes members:
  Member "plug and play priors for bright field electron tomography and sparse interpolation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "plug and play methods provably converge with properly trained denoisers")
  Member "image restoration with locally selected class adapted models":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "kernel free image deblurring with a pair of blurred noisy images")
  Member "algorithm induced prior for image restoration":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "scene adapted plug and play algorithm with guaranteed convergence applications to data fusion in imaging")
  Member "postprocessing of compressed images via sequential denoising":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "near lossless ell_ infty constrained image decompression via deep neural network")
  Member "boosting of image denoising algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep k svd denoising")
  Member "image deblurring and super resolution by adaptive sparse domain selection and adaptive regularization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "improving robustness of deep learning based image reconstruction")
  Member "solving inverse problems with piecewise linear estimators from gaussian mixture models to structured sparsity":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "neumann networks for inverse problems in imaging")

Question: Please tell me which class the source paper belongs to. The 40 classes are: cs.NA(Numerical Analysis), cs.MM(Multimedia), cs.LO(Logic in Computer Science), cs.CY(Computers and Society), cs.CR(Cryptography and Security), cs.DC(Distributed, Parallel, and Cluster Computing), cs.HC(Human-Computer Interaction), cs.CE(Computational Engineering, Finance, and Science), cs.NI(Networking and Internet Architecture), cs.CC(Computational Complexity), cs.AI(Artificial Intelligence), cs.MA(Multiagent Systems), cs.GL(General Literature), cs.NE(Neural and Evolutionary Computing), cs.SC(Symbolic Computation), cs.AR(Hardware Architecture), cs.CV(Computer Vision and Pattern Recognition), cs.GR(Graphics), cs.ET(Emerging Technologies), cs.SY(Systems and Control), cs.CG(Computational Geometry), cs.OH(Other Computer Science), cs.PL(Programming Languages), cs.SE(Software Engineering), cs.LG(Machine Learning), cs.SD(Sound), cs.SI(Social and Information Networks), cs.RO(Robotics), cs.IT(Information Theory), cs.PF(Performance), cs.CL(Computation and Language), cs.IR(Information Retrieval), cs.MS(Mathematical Software), cs.FL(Formal Languages and Automata Theory), cs.DS(Data Structures and Algorithms), cs.OS(Operating Systems), cs.GT(Computer Science and Game Theory), cs.DB(Databases), cs.DL(Digital Libraries), cs.DM(Discrete Mathematics). Directly output the class name.

gpt: cs.CV(Computer Vision and Pattern Recognition)