human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "an online plug and play algorithm for regularized image reconstruction"
  Abstract: Plug-and-play priors (PnP) is a powerful framework for regularizing imaging inverse problems by using advanced denoisers within an iterative algorithm. Recent experimental evidence suggests that PnP algorithms achieve state-of-the-art performance in a range of imaging applications. In this paper, we introduce a new online PnP algorithm based on the iterative shrinkage/thresholding algorithm (ISTA). The proposed algorithm uses only a subset of measurements at every iteration, which makes it scalable to very large datasets. We present a new theoretical convergence analysis, for both batch and online variants of PnP-ISTA, for denoisers that do not necessarily correspond to proximal operators. We also present simulations illustrating the applicability of the algorithm to image reconstruction in diffraction tomography. The results in this paper have the potential to expand the applicability of the PnP framework to very large and redundant datasets.

Connected through hyperedge HE-1 (induced by source paper "plug and play methods provably converge with properly trained denoisers"):
  with members:
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "denoising prior driven deep neural network for image restoration"
    - "regularization by denoising clarifications and new interpretations"
    - "regularized fourier ptychography using an online plug and play algorithm"
    - "lipschitz regularized deep neural networks generalize and are adversarially robust"
    - "plug in estimation in high dimensional linear inverse problems a rigorous analysis"
    - "lipschitz margin training scalable certification of perturbation invariance for deep neural networks"
    - "l2 nonexpansive neural networks"

Connected through hyperedge HE-2 (induced by source paper "super resolution via image adapted denoising cnns incorporating external and internal learning"):
  with members:
    - "deep learning for single image super resolution a brief review"
    - "class aware fully convolutional gaussian and poisson denoising"
    - "recovering realistic texture in image super resolution by deep spatial feature transform"
    - "regularization by denoising clarifications and new interpretations"
    - "the 2018 pirm challenge on perceptual image super resolution"
    - "image super resolution using very deep residual channel attention networks"
    - "a fully progressive approach to single image super resolution"
    - "deep image prior"

Connected through hyperedge HE-3 (induced by source paper "plug and play methods for magnetic resonance imaging"):
  with members:
    - "bilinear recovery using adaptive vector amp"
    - "plug in estimation in high dimensional linear inverse problems a rigorous analysis"
    - "image reconstruction by domain transform manifold learning"
    - "high quality automatic foreground extraction using consensus equilibrium"
    - "learning proximal operators using denoising networks for regularizing inverse imaging problems"
    - "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"
    - "hybrid approximate message passing"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"

Connected through hyperedge HE-4 (induced by source paper "plug and play methods for magnetic resonance imaging long version"):
  with members:
    - "regularization by denoising clarifications and new interpretations"
    - "solving inverse computational imaging problems using deep pixel level prior"
    - "image reconstruction by domain transform manifold learning"
    - "fastmri an open dataset and benchmarks for accelerated mri"
    - "learning proximal operators using denoising networks for regularizing inverse imaging problems"
    - "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "deep convolutional neural network for inverse problems in imaging"

Connected through hyperedge HE-5 (induced by source paper "on the convergence of admm with task adaption and beyond"):
  with members:
    - "fastderain a novel video rain streak removal method using directional gradient priors"
    - "regularization by denoising clarifications and new interpretations"
    - "on the convergence of learning based iterative methods for nonconvex inverse problems"
    - "density aware single image de raining using a multi stream dense network"
    - "a theoretically guaranteed deep optimization framework for robust compressive sensing mri"
    - "toward designing convergent deep operator splitting methods for task specific nonconvex optimization"
    - "clearing the skies a deep network architecture for single image rain removal"
    - "proximal alternating direction network a globally converged deep unrolling framework"

Connected through hyperedge HE-6 (induced by source paper "block coordinate regularization by denoising"):
  with members:
    - "deepred deep image prior powered by red"
    - "regularization by denoising clarifications and new interpretations"
    - "plug in estimation in high dimensional linear inverse problems a rigorous analysis"
    - "the singular values of convolutional layers"
    - "spectral normalization for generative adversarial networks"
    - "fastmri an open dataset and benchmarks for accelerated mri"
    - "deep mean shift priors for image restoration"
    - "learning proximal operators using denoising networks for regularizing inverse imaging problems"

Connected through hyperedge HE-7 (induced by source paper "back projection based fidelity term for ill posed linear inverse problems"):
  with members:
    - "super resolution via image adapted denoising cnns incorporating external and internal learning"
    - "image restoration by iterative denoising and backward projections"
    - "learning deep cnn denoiser prior for image restoration"
    - "plug and play priors for bright field electron tomography and sparse interpolation"
    - "the little engine that could regularization by denoising red"
    - "image restoration and reconstruction using variable splitting and class adapted image priors"
    - "plug and play admm for image restoration fixed point convergence and applications"
    - "tradeoffs between convergence speed and reconstruction accuracy in inverse problems"

Connected through hyperedge HE-8 (induced by source paper "regularization by denoising clarifications and new interpretations"):
  with members:
    - "local kernels that approximate bayesian regularization and proximal operators"
    - "plug in estimation in high dimensional linear inverse problems a rigorous analysis"
    - "scene adapted plug and play algorithm with guaranteed convergence applications to data fusion in imaging"
    - "acceleration of red via vector extrapolation"
    - "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"
    - "hybrid approximate message passing"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "plug and play unplugged optimization free reconstruction using consensus equilibrium"

Question: Please tell me which class the center node 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)