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: "the 2018 pirm challenge on perceptual image super resolution"
  Abstract: This paper reports on the 2018 PIRM challenge on perceptual super-resolution (SR), held in conjunction with the Perceptual Image Restoration and Manipulation (PIRM) workshop at ECCV 2018. In contrast to previous SR challenges, our evaluation methodology jointly quantifies accuracy and perceptual quality, therefore enabling perceptual-driven methods to compete alongside algorithms that target PSNR maximization. Twenty-one participating teams introduced algorithms which well-improved upon the existing state-of-the-art methods in perceptual SR, as confirmed by a human opinion study. We also analyze popular image quality measures and draw conclusions regarding which of them correlates best with human opinion scores. We conclude with an analysis of the current trends in perceptual SR, as reflected from the leading submissions.

Connected through hyperedge HE-1 (induced by source paper "perceptual image super resolution with progressive adversarial network"):
  with members:
    - "toward real world single image super resolution a new benchmark and a new model"
    - "analyzing and improving the image quality of stylegan"
    - "deep learning for image super resolution a survey"
    - "zoom to learn learn to zoom"
    - "camera lens super resolution"
    - "to learn image super resolution use a gan to learn how to do image degradation first"
    - "recovering realistic texture in image super resolution by deep spatial feature transform"
    - "fast accurate and lightweight super resolution with cascading residual network"

Connected through hyperedge HE-2 (induced by source paper "regularized adaptation for stable and efficient continuous level learning on image processing networks"):
  with members:
    - "cfsnet toward a controllable feature space for image restoration"
    - "modulating image restoration with continual levels via adaptive feature modification layers"
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "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 "regularized adaptation for stable and efficient continuous level learning"):
  with members:
    - "cfsnet toward a controllable feature space for image restoration"
    - "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"
    - "residual dense network for image super resolution"
    - "avatar net multi scale zero shot style transfer by feature decoration"
    - "dynamic net tuning the objective without re training for synthesis tasks"

Connected through hyperedge HE-4 (induced by source paper "mri super resolution with gan and 3d multi level densenet smaller faster and better"):
  with members:
    - "esrgan enhanced super resolution generative adversarial networks"
    - "brain mri super resolution using 3d deep densely connected neural networks"
    - "learning a mixture of deep networks for single image super resolution"
    - "on the compactness efficiency and representation of 3d convolutional networks brain parcellation as a pretext task"
    - "enhanced deep residual networks for single image super resolution"
    - "improved training of wasserstein gans"
    - "a survey on deep learning in medical image analysis"
    - "perceptual losses for real time style transfer and super resolution"

Connected through hyperedge HE-5 (induced by source paper "hierarchical auto regressive model for image compression incorporating object saliency and a deep perceptual loss"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "joint autoregressive and hierarchical priors for learned image compression"
    - "generative adversarial networks for extreme learned image compression"
    - "the perception distortion tradeoff"
    - "conditional probability models for deep image compression"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "non local neural networks"
    - "soft to hard vector quantization for end to end learning compressible representations"

Connected through hyperedge HE-6 (induced by source paper "a deep journey into super resolution a survey"):
  with members:
    - "to learn image super resolution use a gan to learn how to do image degradation first"
    - "fast accurate and lightweight super resolution with cascading residual network"
    - "single image super resolution via cascaded multi scale cross network"
    - "fast and accurate single image super resolution via information distillation network"
    - "the relativistic discriminator a key element missing from standard gan"
    - "deep underwater image enhancement"
    - "image super resolution using very deep residual channel attention networks"
    - "esrgan enhanced super resolution generative adversarial networks"

Connected through hyperedge HE-7 (induced by source paper "singan learning a generative model from a single natural image"):
  with members:
    - "surprising effectiveness of few image unsupervised feature learning"
    - "non stationary texture synthesis by adversarial expansion"
    - "ingan capturing and remapping the dna of a natural image"
    - "the perception distortion tradeoff"
    - "sketchygan towards diverse and realistic sketch to image synthesis"
    - "large scale gan training for high fidelity natural image synthesis"
    - "a style based generator architecture for generative adversarial networks"
    - "deep painterly harmonization"

Connected through hyperedge HE-8 (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"
    - "maintaining natural image statistics with the contextual loss"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "adversarial feedback loop"
    - "the perception distortion tradeoff"
    - "conditional probability models for deep image compression"

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)