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 "image restoration by iterative denoising and backward projections"):
  Abstract: Inverse problems appear in many applications such as image deblurring and inpainting. The common approach to address them is to design a specific algorithm for each problem. The Plug-and-Play (P&P) framework, which has been recently introduced, allows solving general inverse problems by leveraging the impressive capabilities of existing denoising algorithms. While this fresh strategy has found many applications, a burdensome parameter tuning is often required in order to obtain high-quality results. In this work, we propose an alternative method for solving inverse problems using denoising algorithms, that requires less parameter tuning. We provide theoretical analysis of the method, and empirically demonstrate that it is competitive with task-specific techniques and the P&P approach for image inpainting and deblurring.

Includes members:
  Member "learning proximal operators using denoising networks for regularizing inverse imaging problems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a novel learnable gradient descent type algorithm for non convex non smooth inverse problems")
  Member "learning deep cnn denoiser prior for image restoration":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sparse weight activation training")
  Member "image restoration using autoencoding priors":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning generative models using denoising density estimators")
  Member "plug and play priors for bright field electron tomography and sparse interpolation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "plug and play methods provably converge with properly trained denoisers")
  Member "the little engine that could regularization by denoising red":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "ada lista learned solvers adaptive to varying models")
  Member "image restoration and reconstruction using variable splitting and class adapted image priors":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "plug and play methods provably converge with properly trained denoisers")
  Member "plug and play admm for image restoration fixed point convergence and applications":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "blind image restoration without prior knowledge")
  Member "raisr rapid and accurate image super resolution":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "super resolution via image adapted denoising cnns incorporating external and internal learning")

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)