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: "ranksrgan generative adversarial networks with ranker for image super resolution"
  Abstract: Generative Adversarial Networks (GAN) have demonstrated the potential to recover realistic details for single image super-resolution (SISR). To further improve the visual quality of super-resolved results, PIRM2018-SR Challenge employed perceptual metrics to assess the perceptual quality, such as PI, NIQE, and Ma. However, existing methods cannot directly optimize these indifferentiable perceptual metrics, which are shown to be highly correlated with human ratings. To address the problem, we propose Super-Resolution Generative Adversarial Networks with Ranker (RankSRGAN) to optimize generator in the direction of perceptual metrics. Specifically, we first train a Ranker which can learn the behavior of perceptual metrics and then introduce a novel rank-content loss to optimize the perceptual quality. The most appealing part is that the proposed method can combine the strengths of different SR methods to generate better results. Extensive experiments show that RankSRGAN achieves visually pleasing results and reaches state-of-the-art performance in perceptual metrics. Project page: this https URL

Connected through hyperedge HE-1 (induced by source paper "closed loop matters dual regression networks for single image super resolution"):
  with members:
    - "intelligent home 3d automatic 3d house design from linguistic descriptions only"
    - "hierarchical neural architecture search for single image super resolution"
    - "discrimination aware network pruning for deep model compression"
    - "multi marginal wasserstein gan"
    - "collaborative unsupervised domain adaptation for medical image diagnosis"
    - "auto embedding generative adversarial networks for high resolution image synthesis"
    - "nat neural architecture transformer for accurate and compact architectures"
    - "image super resolution using very deep residual channel attention networks"

Connected through hyperedge HE-2 (induced by source paper "fd gan generative adversarial networks with fusion discriminator for single image dehazing"):
  with members:
    - "gated fusion network for single image dehazing"
    - "residual dense network for image super resolution"
    - "megadepth learning single view depth prediction from internet photos"
    - "densely connected pyramid dehazing network"
    - "dslr quality photos on mobile devices with deep convolutional networks"
    - "image de raining using a conditional generative adversarial network"
    - "perceptual losses for real time style transfer and super resolution"
    - "dehazenet an end to end system for single image haze removal"

Connected through hyperedge HE-3 (induced by source paper "learning to have an ear for face super resolution"):
  with members:
    - "seeing what a gan cannot generate"
    - "what makes training multi modal networks hard"
    - "toward real world single image super resolution a new benchmark and a new model"
    - "two stream action recognition oriented video super resolution"
    - "image super resolution by neural texture transfer"
    - "evaluating robustness of deep image super resolution against adversarial attacks"
    - "zoom to learn learn to zoom"
    - "speech2face learning the face behind a voice"

Connected through hyperedge HE-4 (induced by source paper "visual privacy protection via mapping distortion"):
  with members:
    - "towards universal object detection by domain attention"
    - "feature selective anchor free module for single shot object detection"
    - "recovering realistic texture in image super resolution by deep spatial feature transform"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "arcface additive angular margin loss for deep face recognition"
    - "pairwise confusion for fine grained visual classification"
    - "siamese instance search for tracking"
    - "ssd single shot multibox detector"

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)