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: "beyond deep residual learning for image restoration persistent homology guided manifold simplification"
  Abstract: The latest deep learning approaches perform better than the state-of-the-art signal processing approaches in various image restoration tasks. However, if an image contains many patterns and structures, the performance of these CNNs is still inferior. To address this issue, here we propose a novel feature space deep residual learning algorithm that outperforms the existing residual learning. The main idea is originated from the observation that the performance of a learning algorithm can be improved if the input and/or label manifolds can be made topologically simpler by an analytic mapping to a feature space. Our extensive numerical studies using denoising experiments and NTIRE single-image super-resolution (SISR) competition demonstrate that the proposed feature space residual learning outperforms the existing state-of-the-art approaches. Moreover, our algorithm was ranked third in NTIRE competition with 5-10 times faster computational time compared to the top ranked teams. The source code is available on page : this https URL

Connected through hyperedge HE-1 (induced by source paper "multi level wavelet convolutional neural networks"):
  with members:
    - "parallel grid pooling for data augmentation"
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "image super resolution using very deep residual channel attention networks"
    - "toward convolutional blind denoising of real photographs"
    - "framing u net via deep convolutional framelets application to sparse view ct"
    - "deep laplacian pyramid networks for fast and accurate super resolution"
    - "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"
    - "enhanced deep residual networks for single image super resolution"

Connected through hyperedge HE-2 (induced by source paper "real image denoising with feature attention"):
  with members:
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "a trilateral weighted sparse coding scheme for real world image denoising"
    - "image super resolution using very deep residual channel attention networks"
    - "toward convolutional blind denoising of real photographs"
    - "neural nearest neighbors networks"
    - "real world noisy image denoising a new benchmark"
    - "renoir a dataset for real low light image noise reduction"
    - "unprocessing images for learned raw denoising"

Connected through hyperedge HE-3 (induced by source paper "photorealistic style transfer via wavelet transforms"):
  with members:
    - "a closed form solution to photorealistic image stylization"
    - "arbitrary style transfer with deep feature reshuffle"
    - "nsml meet the mlaas platform with a real world case study"
    - "framing u net via deep convolutional framelets application to sparse view ct"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "scaling the scattering transform deep hybrid networks"
    - "wavelet convolutional neural networks for texture classification"
    - "visual attribute transfer through deep image analogy"

Connected through hyperedge HE-4 (induced by source paper "the perception distortion tradeoff"):
  with members:
    - "singan learning a generative model from a single natural image"
    - "rate distortion perception tradeoff of variable length source coding for general information sources"
    - "rethinking lossy compression the rate distortion perception tradeoff"
    - "generative adversarial networks for extreme learned image compression"
    - "maintaining natural image statistics with the contextual loss"
    - "the 2018 pirm challenge on perceptual image super resolution"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "introducing the perception distortion tradeoff into the rate distortion theory of general information sources"

Connected through hyperedge HE-5 (induced by source paper "multi level wavelet cnn for image restoration"):
  with members:
    - "framing u net via deep convolutional framelets application to sparse view ct"
    - "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"
    - "structure preserving image super resolution via contextualized multitask learning"
    - "learning deep cnn denoiser prior for image restoration"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "understanding convolution for semantic segmentation"
    - "memnet a persistent memory network for image restoration"
    - "learning convolutional networks for content weighted image compression"

Connected through hyperedge HE-6 (induced by source paper "image super resolution via dual state recurrent networks"):
  with members:
    - "deep laplacian pyramid networks for fast and accurate super resolution"
    - "dual path networks"
    - "sharing residual units through collective tensor factorization in deep neural networks"
    - "accelerating the super resolution convolutional neural network"
    - "deep networks with stochastic depth"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "densely connected convolutional networks"
    - "real time single image and video super resolution using an efficient sub pixel convolutional neural network"

Connected through hyperedge HE-7 (induced by source paper "a multiscale image denoising algorithm based on dilated residual convolution network"):
  with members:
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "dilated deep residual network for image denoising"
    - "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"
    - "dilated residual networks"
    - "learning deep cnn denoiser prior for image restoration"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "understanding convolution for semantic segmentation"
    - "identity mappings in deep residual networks"

Connected through hyperedge HE-8 (induced by source paper "toward bridging the simulated to real gap benchmarking super resolution on real data"):
  with members:
    - "esrgan enhanced super resolution generative adversarial networks"
    - "the perception distortion tradeoff"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "deep laplacian pyramid networks for fast and accurate super resolution"
    - "enhanced deep residual networks for single image super resolution"
    - "benchmarking denoising algorithms with real photographs"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "raisr rapid and accurate image super resolution"

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)