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 for image super resolution a survey"
  Abstract: Image Super-Resolution (SR) is an important class of image processing techniques to enhance the resolution of images and videos in computer vision. Recent years have witnessed remarkable progress of image super-resolution using deep learning techniques. In this survey, we aim to give a survey on recent advances of image super-resolution techniques using deep learning approaches in a systematic way. In general, we can roughly group the existing studies of SR techniques into three major categories: supervised SR, unsupervised SR, and domain-specific SR. In addition, we also cover some other important issues, such as publicly available benchmark datasets and performance evaluation metrics. Finally, we conclude this survey by highlighting several future directions and open issues which should be further addressed by the community in the future.

Connected through hyperedge HE-1 (induced by source paper "learning enriched features for real image restoration and enhancement"):
  with members:
    - "toward real world single image super resolution a new benchmark and a new model"
    - "kindling the darkness a practical low light image enhancer"
    - "residual non local attention networks for image restoration"
    - "making convolutional networks shift invariant again"
    - "a deep journey into super resolution a survey"
    - "selective kernel networks"
    - "real image denoising with feature attention"
    - "deep high resolution representation learning for human pose estimation"

Connected through hyperedge HE-2 (induced by source paper "adversarial perturbations prevail in the y channel of the ycbcr color space"):
  with members:
    - "jumprelu a retrofit defense strategy for adversarial attacks"
    - "an empirical investigation of randomized defenses against adversarial attacks"
    - "robust neural networks using randomized adversarial training"
    - "certified adversarial robustness via randomized smoothing"
    - "deep learning for single image super resolution a brief review"
    - "deep learning for multiple image super resolution"
    - "sampling and super resolution of sparse signals beyond the fourier domain"
    - "a kernelized manifold mapping to diminish the effect of adversarial perturbations"

Connected through hyperedge HE-3 (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"
    - "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"
    - "the 2018 pirm challenge on perceptual image super resolution"

Connected through hyperedge HE-4 (induced by source paper "highres net recursive fusion for multi frame super resolution of satellite imagery"):
  with members:
    - "tackling climate change with machine learning"
    - "learning parallax attention for stereo image super resolution"
    - "frame and feature context video super resolution"
    - "edvr video restoration with enhanced deformable convolutional networks"
    - "learning disentangled representations of satellite image time series"
    - "to learn image super resolution use a gan to learn how to do image degradation first"
    - "mapping informal settlements in developing countries with multi resolution multi spectral data"
    - "perceptual losses for real time style transfer and super resolution"

Connected through hyperedge HE-5 (induced by source paper "lightweight image super resolution with adaptive weighted learning network"):
  with members:
    - "fast accurate and lightweight super resolution with neural architecture search"
    - "multi objective reinforced evolution in mobile neural architecture search"
    - "fast accurate and lightweight super resolution with cascading residual network"
    - "residual convolutional neural network revisited with active weighted mapping"
    - "image super resolution using very deep residual channel attention networks"
    - "wide activation for efficient and accurate image super resolution"
    - "residual dense network for image super resolution"
    - "deep back projection networks for super resolution"

Connected through hyperedge HE-6 (induced by source paper "fast bayesian uncertainty estimation of batch normalized single image super resolution network"):
  with members:
    - "inherent brain segmentation quality control from fully convnet monte carlo sampling"
    - "learning to decode 7t like mr image reconstruction from 3t mr images"
    - "residual dense network for image super resolution"
    - "densely connected pyramid dehazing network"
    - "fully convolutional model for variable bit length and lossy high density compression of mammograms"
    - "sketch based manga retrieval using manga109 dataset"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "perceptual losses for real time style transfer and super resolution"

Connected through hyperedge HE-7 (induced by source paper "super resolved image perceptual quality improvement via multi feature discriminators"):
  with members:
    - "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"
    - "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-8 (induced by source paper "cascaded detail preserving networks for super resolution of document images"):
  with members:
    - "the contextual loss for image transformation with non aligned data"
    - "perceptual losses for real time style transfer and super resolution"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "real time single image and video super resolution using an efficient sub pixel convolutional neural network"
    - "holistically nested edge detection"
    - "accurate image super resolution using very deep convolutional networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

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)