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: "learning to maintain natural image statistics"
  Abstract: Maintaining natural image statistics is a crucial factor in restoration and generation of realistic looking images. When training CNNs, photorealism is usually attempted by adversarial training (GAN), that pushes the output images to lie on the manifold of natural images. GANs are very powerful, but not perfect. They are hard to train and the results still often suffer from artifacts. In this paper we propose a complementary approach, whose goal is to train a feed-forward CNN to maintain natural internal statistics. We look explicitly at the distribution of features in an image and train the network to generate images with natural feature distributions. Our approach reduces by orders of magnitude the number of images required for training and achieves state-of-the-art results on both single-image super-resolution, and high-resolution surface normal estimation.

Connected through hyperedge HE-1 (induced by source paper "creating high resolution images with a latent adversarial generator"):
  with members:
    - "to learn image super resolution use a gan to learn how to do image degradation first"
    - "the relativistic discriminator a key element missing from standard gan"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "the perception distortion tradeoff"
    - "nima neural image assessment"
    - "a fully progressive approach to single image super resolution"
    - "enhanced deep residual networks for single image super resolution"
    - "enhancenet single image super resolution through automated texture synthesis"

Connected through hyperedge HE-2 (induced by source paper "deep learning for single image super resolution a brief review"):
  with members:
    - "recovering realistic texture in image super resolution by deep spatial feature transform"
    - "fast accurate and lightweight super resolution with cascading residual network"
    - "fast and accurate single image super resolution via information distillation network"
    - "super resolution based on image adapted cnn denoisers incorporating generalization of training data and internal learning in test time"
    - "unsupervised image super resolution using cycle in cycle generative adversarial networks"
    - "the perception distortion tradeoff"
    - "super resolution via conditional implicit maximum likelihood estimation"
    - "residual dense network for image super resolution"

Connected through hyperedge HE-3 (induced by source paper "cfsnet toward a controllable feature space for image restoration"):
  with members:
    - "lightweight feature fusion network for single image super resolution"
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "maintaining natural image statistics with the contextual loss"
    - "the 2018 pirm challenge on perceptual image super resolution"
    - "image super resolution using very deep residual channel attention networks"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "toward convolutional blind denoising of real photographs"
    - "the perception distortion tradeoff"

Connected through hyperedge HE-4 (induced by source paper "srobb targeted perceptual loss for single image super resolution"):
  with members:
    - "recovering realistic texture in image super resolution by deep spatial feature transform"
    - "the 2018 pirm challenge on perceptual image super resolution"
    - "the unreasonable effectiveness of texture transfer for single image super resolution"
    - "image super resolution using very deep residual channel attention networks"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "unsupervised image super resolution using cycle in cycle generative adversarial networks"
    - "crafting a toolchain for image restoration by deep reinforcement learning"
    - "the unreasonable effectiveness of deep features as a perceptual metric"

Connected through hyperedge HE-5 (induced by source paper "benefiting from multitask learning to improve single image super resolution"):
  with members:
    - "learn to synthesize and synthesize to learn"
    - "using photorealistic face synthesis and domain adaptation to improve facial expression analysis"
    - "recovering realistic texture in image super resolution by deep spatial feature transform"
    - "image super resolution using very deep residual channel attention networks"
    - "efficient active learning for image classification and segmentation using a sample selection and conditional generative adversarial network"
    - "video deblurring via semantic segmentation and pixel wise non linear kernel"
    - "srpgan perceptual generative adversarial network for single image super resolution"
    - "photographic image synthesis with cascaded refinement networks"

Connected through hyperedge HE-6 (induced by source paper "learning likelihoods with conditional normalizing flows"):
  with members:
    - "integer discrete flows and lossless compression"
    - "emerging convolutions for generative normalizing flows"
    - "guided image generation with conditional invertible neural networks"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "unsupervised image super resolution using cycle in cycle generative adversarial networks"
    - "ffjord free form continuous dynamics for scalable reversible generative models"
    - "pixelcnn improving the pixelcnn with discretized logistic mixture likelihood and other modifications"
    - "a downsampled variant of imagenet as an alternative to the cifar datasets"

Connected through hyperedge HE-7 (induced by source paper "art2real unfolding the reality of artworks via semantically aware image to image translation"):
  with members:
    - "a style aware content loss for real time hd style transfer"
    - "multimodal unsupervised image to image translation"
    - "shift net image inpainting via deep feature rearrangement"
    - "generative image inpainting with contextual attention"
    - "drit diverse image to image translation via disentangled representations"
    - "dual motion gan for future flow embedded video prediction"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "decomposing motion and content for natural video sequence prediction"

Connected through hyperedge HE-8 (induced by source paper "esrgan enhanced super resolution generative adversarial networks"):
  with members:
    - "recovering realistic texture in image super resolution by deep spatial feature transform"
    - "residual networks of residual networks multilevel residual networks"
    - "the relativistic discriminator a key element missing from standard gan"
    - "the 2018 pirm challenge on perceptual image super resolution"
    - "image super resolution using very deep residual channel attention networks"
    - "unsupervised image super resolution using cycle in cycle generative adversarial networks"
    - "the perception distortion tradeoff"
    - "spectral normalization for generative 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)