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: "maintaining natural image statistics with the contextual loss"
  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, that could be applied with or without GAN, 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 "rethinking lossy compression the rate distortion perception tradeoff"):
  with members:
    - "rate distortion perception tradeoff of variable length source coding for general information sources"
    - "joint autoregressive and hierarchical priors for learned image compression"
    - "generative adversarial networks for extreme learned image compression"
    - "the 2018 pirm challenge on perceptual image super resolution"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "adversarial feedback loop"
    - "the perception distortion tradeoff"
    - "conditional probability models for deep image compression"

Connected through hyperedge HE-2 (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"
    - "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"
    - "non local recurrent network for image restoration"

Connected through hyperedge HE-3 (induced by source paper "progressive perception oriented network for single image super resolution"):
  with members:
    - "feedback network for image super resolution"
    - "espnet efficient spatial pyramid of dilated convolutions for semantic segmentation"
    - "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"
    - "the relativistic discriminator a key element missing from standard gan"
    - "the 2018 pirm challenge on perceptual image super resolution"
    - "esrgan enhanced super resolution generative adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "super resolved image perceptual quality improvement via multi feature discriminators"):
  with members:
    - "deep learning for image super resolution a survey"
    - "the 2018 pirm challenge on perceptual image super resolution"
    - "image super resolution using very deep residual channel attention networks"
    - "generative adversarial network based image super resolution using perceptual content losses"
    - "deep back projection networks for super resolution"
    - "enhanced deep residual networks for single image super resolution"
    - "perceptual losses for real time style transfer and super resolution"
    - "photo realistic single image super resolution using a generative adversarial network"

Connected through hyperedge HE-5 (induced by source paper "a hvs inspired attention to improve loss metrics for cnn based perception oriented super resolution"):
  with members:
    - "deep neural networks for no reference and full reference image quality assessment"
    - "the perception distortion tradeoff"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "perceptual losses for real time style transfer and super resolution"
    - "learning a no reference quality metric for single image super resolution"
    - "accurate image super resolution using very deep convolutional networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-6 (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"
    - "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"
    - "deep generative models for distribution preserving lossy compression"

Connected through hyperedge HE-7 (induced by source paper "deep learning based image super resolution considering quantitative and perceptual quality"):
  with members:
    - "deep learning for single image super resolution a brief review"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "the 2018 pirm challenge on perceptual image super resolution"
    - "image super resolution using very deep residual channel attention networks"
    - "the perception distortion tradeoff"
    - "nima neural image assessment"
    - "residual dense network for image super resolution"
    - "the contextual loss for image transformation with non aligned data"

Connected through hyperedge HE-8 (induced by source paper "dynamic net tuning the objective without re training"):
  with members:
    - "facelet bank for fast portrait manipulation"
    - "non stationary texture synthesis by adversarial expansion"
    - "the 2018 pirm challenge on perceptual image super resolution"
    - "the perception distortion tradeoff"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "the contextual loss for image transformation with non aligned data"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "gradnorm gradient normalization for adaptive loss balancing in deep multitask 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)