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 learning based image super resolution considering quantitative and perceptual quality"
  Abstract: Recently, it has been shown that in super-resolution, there exists a tradeoff relationship between the quantitative and perceptual quality of super-resolved images, which correspond to the similarity to the ground-truth images and the naturalness, respectively. In this paper, we propose a novel super-resolution method that can improve the perceptual quality of the upscaled images while preserving the conventional quantitative performance. The proposed method employs a deep network for multi-pass upscaling in company with a discriminator network and two quantitative score predictor networks. Experimental results demonstrate that the proposed method achieves a good balance of the quantitative and perceptual quality, showing more satisfactory results than existing methods.

Connected through hyperedge HE-1 (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"
    - "maintaining natural image statistics with the contextual loss"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "ranksrgan generative adversarial networks with ranker for image super resolution"):
  with members:
    - "blind super resolution with iterative kernel correction"
    - "modulating image restoration with continual levels via adaptive feature modification layers"
    - "suppressing model overfitting for image super resolution networks"
    - "deep neural networks for no reference and full reference image quality assessment"
    - "recovering realistic texture in image super resolution by deep spatial feature transform"
    - "the 2018 pirm challenge on perceptual image super resolution"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "the perception distortion tradeoff"

Connected through hyperedge HE-3 (induced by source paper "evaluating robustness of deep image super resolution against adversarial attacks"):
  with members:
    - "image super resolution as a defense against adversarial attacks"
    - "fast accurate and lightweight super resolution with cascading residual network"
    - "image super resolution using very deep residual channel attention networks"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "the perception distortion tradeoff"
    - "is robustness the cost of accuracy a comprehensive study on the robustness of 18 deep image classification models"
    - "enhanced deep residual networks for single image super resolution"
    - "mitigating adversarial effects through randomization"

Connected through hyperedge HE-4 (induced by source paper "the 2018 pirm challenge on perceptual image super resolution"):
  with members:
    - "deep learning for single image super resolution a brief review"
    - "recovering realistic texture in image super resolution by deep spatial feature transform"
    - "the relativistic discriminator a key element missing from standard gan"
    - "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"
    - "generative adversarial network based image super resolution using perceptual content losses"
    - "the perception distortion tradeoff"

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)