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: "poisson inverse problems by the plug and play scheme"
  Abstract: The Anscombe transform offers an approximate conversion of a Poisson random variable into a Gaussian one. This transform is important and appealing, as it is easy to compute, and becomes handy in various inverse problems with Poisson noise contamination. Solution to such problems can be done by first applying the Anscombe transform, then applying a Gaussian-noise-oriented restoration algorithm of choice, and finally applying an inverse Anscombe transform. The appeal in this approach is due to the abundance of high-performance restoration algorithms designed for white additive Gaussian noise (we will refer to these hereafter as "Gaussian-solvers"). This process is known to work well for high SNR images, where the Anscombe transform provides a rather accurate approximation. When the noise level is high, the above path loses much of its effectiveness, and the common practice is to replace it with a direct treatment of the Poisson distribution. Naturally, with this we lose the ability to leverage on vastly available Gaussian-solvers. #R##N#In this work we suggest a novel method for coupling Gaussian denoising algorithms to Poisson noisy inverse problems, which is based on a general approach termed "Plug-and-Play". Deploying the Plug-and-Play approach to such problems leads to an iterative scheme that repeats several key steps: 1) A convex programming task of simple form that can be easily treated; 2) A powerful Gaussian denoising algorithm of choice; and 3) A simple update step. #R##N#Such a modular method, just like the Anscombe transform, enables other developers to plug their own Gaussian denoising algorithms to our scheme in an easy way. While the proposed method bares some similarity to the Anscombe operation, it is in fact based on a different mathematical basis, which holds true for all SNR ranges.

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 "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"
    - "regularization by denoising clarifications and new interpretations"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "deepred deep image prior powered by red"):
  with members:
    - "a bayesian perspective on the deep image prior"
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "image super resolution using very deep residual channel attention networks"
    - "neural nearest neighbors networks"
    - "non local recurrent network for image restoration"
    - "image restoration using total variation regularized deep image prior"
    - "deep laplacian pyramid networks for fast and accurate super resolution"
    - "deep image prior"

Connected through hyperedge HE-4 (induced by source paper "linearized admm and fast nonlocal denoising for efficient plug and play restoration"):
  with members:
    - "plug and play priors for bright field electron tomography and sparse interpolation"
    - "image restoration and reconstruction using variable splitting and class adapted image priors"
    - "plug and play admm for image restoration fixed point convergence and applications"
    - "compressive imaging via approximate message passing with image denoising"
    - "james stein type center pixel weights for non local means image denoising"

Connected through hyperedge HE-5 (induced by source paper "class aware fully convolutional gaussian and poisson denoising"):
  with members:
    - "class specific image denoising using importance sampling"
    - "image restoration by iterative denoising and backward projections"
    - "deep burst denoising"
    - "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"
    - "blind image deblurring using class adapted image priors"
    - "learning deep cnn denoiser prior for image restoration"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "benchmarking denoising algorithms with real photographs"

Connected through hyperedge HE-6 (induced by source paper "external patch based image restoration using importance sampling"):
  with members:
    - "poisson image denoising using best linear prediction a post processing framework"
    - "class specific image denoising using importance sampling"
    - "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 class aware denoising"
    - "deep convolutional denoising of low light images"
    - "image restoration and reconstruction using variable splitting and class adapted image priors"
    - "deep gaussian conditional random field network a model based deep network for discriminative denoising"

Connected through hyperedge HE-7 (induced by source paper "learning convex regularizers for optimal bayesian denoising"):
  with members:
    - "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"
    - "plug and play priors for bright field electron tomography and sparse interpolation"
    - "learning optimal nonlinearities for iterative thresholding algorithms"
    - "plug and play admm for image restoration fixed point convergence and applications"
    - "non local color image denoising with convolutional neural networks"
    - "on learning optimized reaction diffusion processes for effective image restoration"
    - "sparse stochastic processes and discretization of linear inverse problems"
    - "bayesian estimation for continuous time sparse stochastic processes"

Connected through hyperedge HE-8 (induced by source paper "system aware compression"):
  with members:
    - "optimized pre compensating compression"
    - "restoration by compression"
    - "the little engine that could regularization by denoising red"
    - "distortion rate function of sub nyquist sampled gaussian sources"
    - "postprocessing of compressed images via sequential denoising"

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)