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 "plug and play admm for image restoration fixed point convergence and applications"):
  Abstract: Alternating direction method of multiplier (ADMM) is a widely used algorithm for solving constrained optimization problems in image restoration. Among many useful features, one critical feature of the ADMM algorithm is its modular structure which allows one to plug in any off-the-shelf image denoising algorithm for a subproblem in the ADMM algorithm. Because of the plug-in nature, this type of ADMM algorithms is coined the name "Plug-and-Play ADMM". Plug-and-Play ADMM has demonstrated promising empirical results in a number of recent papers. However, it is unclear under what conditions and by using what denoising algorithms would it guarantee convergence. Also, since Plug-and-Play ADMM uses a specific way to split the variables, it is unclear if fast implementation can be made for common Gaussian and Poissonian image restoration problems. #R##N#In this paper, we propose a Plug-and-Play ADMM algorithm with provable fixed point convergence. We show that for any denoising algorithm satisfying an asymptotic criteria, called bounded denoisers, Plug-and-Play ADMM converges to a fixed point under a continuation scheme. We also present fast implementations for two image restoration problems on super-resolution and single-photon imaging. We compare Plug-and-Play ADMM with state-of-the-art algorithms in each problem type, and demonstrate promising experimental results of the algorithm.

Includes members:
  Member "approximate message passing algorithm with universal denoising and gaussian mixture learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "analysis of approximate message passing with non separable denoisers and markov random field priors")
  Member "plug and play priors for bright field electron tomography and sparse interpolation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "plug and play methods provably converge with properly trained denoisers")
  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 "poisson inverse problems by the plug and play scheme":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "plug and play methods provably converge with properly trained denoisers")
  Member "compressive imaging via approximate message passing with image denoising":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "block coordinate regularization by denoising")
  Member "group based sparse representation for image restoration":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a benchmark for sparse coding when group sparsity meets rank minimization")
  Member "from denoising to compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a novel learnable gradient descent type algorithm for non convex non smooth inverse problems")

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)