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: "regularization by denoising clarifications and new interpretations"
  Abstract: Regularization by Denoising (RED), as recently proposed by Romano, Elad, and Milanfar, is powerful image-recovery framework that aims to minimize an explicit regularization objective constructed from a plug-in image-denoising function. Experimental evidence suggests that the RED algorithms are state-of-the-art. We claim, however, that explicit regularization does not explain the RED algorithms. In particular, we show that many of the expressions in the paper by Romano et al. hold only when the denoiser has a symmetric Jacobian, and we demonstrate that such symmetry does not occur with practical denoisers such as non-local means, BM3D, TNRD, and DnCNN. To explain the RED algorithms, we propose a new framework called Score-Matching by Denoising (SMD), which aims to match a "score" (i.e., the gradient of a log-prior). We then show tight connections between SMD, kernel density estimation, and constrained minimum mean-squared error denoising. Furthermore, we interpret the RED algorithms from Romano et al. and propose new algorithms with acceleration and convergence guarantees. Finally, we show that the RED algorithms seek a consensus equilibrium solution, which facilitates a comparison to plug-and-play ADMM.

Connected through hyperedge HE-1 (induced by source paper "bilinear recovery using adaptive vector amp"):
  with members:
    - "plug in estimation in high dimensional linear inverse problems a rigorous analysis"
    - "near optimal matrix recovery from random linear measurements"
    - "rigorous dynamics of expectation propagation based signal recovery from unitarily invariant measurements"
    - "additivity of information in multilayer networks via additive gaussian noise transforms"
    - "hybrid approximate message passing"
    - "state evolution for approximate message passing with non separable functions"
    - "the replica symmetric prediction for compressed sensing with gaussian matrices is exact"
    - "expectation consistent approximate inference generalizations and convergence"

Connected through hyperedge HE-2 (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"
    - "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"
    - "neural nearest neighbors networks"

Connected through hyperedge HE-3 (induced by source paper "deep plug and play super resolution for arbitrary blur kernels"):
  with members:
    - "learning converged propagations with deep prior ensemble for image enhancement"
    - "gated fusion network for joint image deblurring and super resolution"
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "image super resolution using very deep residual channel attention networks"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "neural nearest neighbors networks"
    - "deep cnn denoiser and multi layer neighbor component embedding for face hallucination"
    - "residual dense network for image super resolution"

Connected through hyperedge HE-4 (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"
    - "the 2018 pirm challenge on perceptual image super resolution"
    - "image super resolution using very deep residual channel attention networks"
    - "an online plug and play algorithm for regularized image reconstruction"
    - "a fully progressive approach to single image super resolution"
    - "deep image prior"

Connected through hyperedge HE-5 (induced by source paper "plug and play methods for magnetic resonance imaging long version"):
  with members:
    - "an online plug and play algorithm for regularized image reconstruction"
    - "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-6 (induced by source paper "deep plug and play prior for low rank tensor completion"):
  with members:
    - "plug and play methods provably converge with properly trained denoisers"
    - "deep plug and play super resolution for arbitrary blur kernels"
    - "low rank tensor completion for multiway visual data"
    - "non local meets global an integrated paradigm for hyperspectral denoising"
    - "tensor n tubal rank and its convex relaxation for low rank tensor recovery"
    - "tensor robust principal component analysis exact recovery of corrupted low rank tensors via convex optimization"
    - "image restoration by iterative denoising and backward projections"
    - "learning deep cnn denoiser prior for image restoration"

Connected through hyperedge HE-7 (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"
    - "on the convergence of learning based iterative methods for nonconvex inverse problems"
    - "an online plug and play algorithm for regularized image reconstruction"
    - "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-8 (induced by source paper "block coordinate regularization by denoising"):
  with members:
    - "deepred deep image prior powered by red"
    - "plug in estimation in high dimensional linear inverse problems a rigorous analysis"
    - "the singular values of convolutional layers"
    - "spectral normalization for generative adversarial networks"
    - "an online plug and play algorithm for regularized image reconstruction"
    - "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"

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)