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: "postprocessing of compressed images via sequential denoising"
  Abstract: In this paper, we propose a novel postprocessing technique for compression-artifact reduction. Our approach is based on posing this task as an inverse problem, with a regularization that leverages on existing state-of-the-art image denoising algorithms. We rely on the recently proposed Plug-and-Play Prior framework, suggesting the solution of general inverse problems via alternating direction method of multipliers, leading to a sequence of Gaussian denoising steps. A key feature in our scheme is a linearization of the compression-decompression process, so as to get a formulation that can be optimized. In addition, we supply a thorough analysis of this linear approximation for several basic compression procedures. The proposed method is suitable for diverse compression techniques that rely on transform coding. In particular, we demonstrate impressive gains in image quality for several leading compression methods-JPEG, JPEG2000, and HEVC.

Connected through hyperedge HE-1 (induced by source paper "near lossless ell_ infty constrained image decompression via deep neural network"):
  with members:
    - "joint autoregressive and hierarchical priors for learned image compression"
    - "generative adversarial networks for extreme learned image compression"
    - "conditional probability models for deep image compression"
    - "deep back projection networks for super resolution"
    - "dilated deep residual network for image denoising"
    - "enhanced deep residual networks for single image super resolution"
    - "deep generative adversarial compression artifact removal"
    - "single image super resolution with dilated convolution based multi scale information learning inception module"

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"
    - "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-3 (induced by source paper "near lossless l infinity constrained multi rate image decompression via deep neural network"):
  with members:
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "deep generative adversarial compression artifact removal"
    - "compression artifacts removal using convolutional neural networks"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "improved techniques for training gans"
    - "generative adversarial text to image synthesis"
    - "one to many network for visually pleasing compression artifacts reduction"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "dpw sdnet dual pixel wavelet domain deep cnns for soft decoding of jpeg compressed images"):
  with members:
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "cas cnn a deep convolutional neural network for image compression artifact suppression"
    - "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"
    - "random walk graph laplacian based smoothness prior for soft decoding of jpeg images"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "deep generative adversarial compression artifact removal"
    - "one to many network for visually pleasing compression artifacts reduction"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-5 (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"
    - "poisson inverse problems by the plug and play scheme"

Connected through hyperedge HE-6 (induced by source paper "optimized pre compensating compression"):
  with members:
    - "restoration by compression"
    - "the little engine that could regularization by denoising red"
    - "plug and play admm for image restoration fixed point convergence and applications"
    - "poisson inverse problems by the plug and play scheme"
    - "motion compensated coding and frame rate up conversion models and analysis"

Connected through hyperedge HE-7 (induced by source paper "deep class aware denoising"):
  with members:
    - "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"
    - "the little engine that could regularization by denoising red"
    - "plug and play admm for image restoration fixed point convergence and applications"
    - "context encoders feature learning by inpainting"
    - "colorful image colorization"
    - "deep gaussian conditional random field network a model based deep network for discriminative denoising"
    - "poisson inverse problems by the plug and play scheme"
    - "deep learning in neural networks"

Connected through hyperedge HE-8 (induced by source paper "deep generative adversarial compression artifact removal"):
  with members:
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "pixel recursive super resolution"
    - "perceptual losses for real time style transfer and super resolution"
    - "compression artifacts removal using convolutional neural networks"
    - "generating images with perceptual similarity metrics based on deep networks"
    - "improved techniques for training gans"
    - "image restoration using very deep convolutional encoder decoder networks with symmetric skip connections"
    - "accurate image super resolution using very deep convolutional networks"

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)