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: "deep mean shift priors for image restoration"
  Abstract: In this paper we introduce a natural image prior that directly represents a Gaussian-smoothed version of the natural image distribution. We include our prior in a formulation of image restoration as a Bayes estimator that also allows us to solve noise-blind image restoration problems. We show that the gradient of our prior corresponds to the mean-shift vector on the natural image distribution. In addition, we learn the mean-shift vector field using denoising autoencoders, and use it in a gradient descent approach to perform Bayes risk minimization. We demonstrate competitive results for noise-blind deblurring, super-resolution, and demosaicing.

Connected through hyperedge HE-1 (induced by source paper "learning generative models using denoising density estimators"):
  with members:
    - "generative modeling by estimating gradients of the data distribution"
    - "a style based generator architecture for generative adversarial networks"
    - "ffjord free form continuous dynamics for scalable reversible generative models"
    - "implicit maximum likelihood estimation"
    - "progressive growing of gans for improved quality stability and variation"
    - "searching for activation functions"
    - "improved training of wasserstein gans"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"

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 "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"
    - "an online plug and play algorithm for regularized image reconstruction"
    - "fastmri an open dataset and benchmarks for accelerated mri"
    - "learning proximal operators using denoising networks for regularizing inverse imaging problems"

Connected through hyperedge HE-4 (induced by source paper "proximal splitting networks for image restoration"):
  with members:
    - "deep laplacian pyramid networks for fast and accurate super resolution"
    - "learning proximal operators using denoising networks for regularizing inverse imaging problems"
    - "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"
    - "one network to solve them all solving linear inverse problems using deep projection models"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "accelerating the super resolution convolutional neural network"
    - "deep multi scale convolutional neural network for dynamic scene deblurring"
    - "laplacian pyramid reconstruction and refinement for semantic segmentation"

Connected through hyperedge HE-5 (induced by source paper "gridface face rectification via learning local homography transformations"):
  with members:
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "reconstruction based disentanglement for pose invariant face recognition"
    - "learning proximal operators using denoising networks for regularizing inverse imaging problems"
    - "quality aware network for set to set recognition"
    - "toward end to end face recognition through alignment learning"
    - "synthesizing normalized faces from facial identity features"
    - "towards large pose face frontalization in the wild"
    - "neural aggregation network for video face recognition"

Connected through hyperedge HE-6 (induced by source paper "neural nearest neighbors networks"):
  with members:
    - "a trilateral weighted sparse coding scheme for real world image denoising"
    - "toward convolutional blind denoising of real photographs"
    - "non local recurrent network for image restoration"
    - "attention is all you need"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "benchmarking denoising algorithms with real photographs"
    - "dirty pixels optimizing image classification architectures for raw sensor data"
    - "photo realistic single image super resolution using a generative adversarial network"

Connected through hyperedge HE-7 (induced by source paper "deep image demosaicking using a cascade of convolutional residual denoising networks"):
  with members:
    - "learning deep cnn denoiser prior for image restoration"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "universal denoising networks a novel cnn architecture for image denoising"
    - "the little engine that could regularization by denoising red"
    - "deep residual learning for image recognition"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

Connected through hyperedge HE-8 (induced by source paper "disentangling factors of variation by mixing them"):
  with members:
    - "began boundary equilibrium generative adversarial networks"
    - "variational inference of disentangled latent concepts from unlabeled observations"
    - "progressive growing of gans for improved quality stability and variation"
    - "unsupervised learning of disentangled representations from video"
    - "unsupervised learning of depth and ego motion from video"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "neural face editing with intrinsic image disentangling"
    - "unpaired image to image translation using cycle consistent adversarial 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)