human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "super resolution using convolutional neural networks without any checkerboard artifacts"):
  Abstract: It is well-known that a number of excellent super-resolution (SR) methods using convolutional neural networks (CNNs) generate checkerboard artifacts. A condition to avoid the checkerboard artifacts is proposed in this paper. So far, checkerboard artifacts have been mainly studied for linear multirate systems, but the condition to avoid checkerboard artifacts can not be applied to CNNs due to the non-linearity of CNNs. We extend the avoiding condition for CNNs, and apply the proposed structure to some typical SR methods to confirm the effectiveness of the new scheme. Experiment results demonstrate that the proposed structure can perfectly avoid to generate checkerboard artifacts under two loss conditions: mean square error and perceptual loss, while keeping excellent properties that the SR methods have.

Includes members:
  Member "pixel deconvolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "pixel level dense prediction without decoder")
  Member "deep laplacian pyramid networks for fast and accurate super resolution":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "perceptual image super resolution with progressive adversarial network")
  Member "checkerboard artifact free sub pixel convolution a note on sub pixel convolution resize convolution and convolution resize":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "fisheyedistancenet self supervised scale aware distance estimation using monocular fisheye camera for autonomous driving")
  Member "perceptual losses for real time style transfer and super resolution":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "gan compression efficient architectures for interactive conditional gans")
  Member "photo realistic single image super resolution using a generative adversarial network":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "real time single image and video super resolution using an efficient sub pixel convolutional neural network":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "adversarial perturbations prevail in the y channel of the ycbcr color space")
  Member "enhancenet single image super resolution through automated texture synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "perceptual image super resolution with progressive adversarial network")
  Member "deeply recursive convolutional network for image super resolution":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning enriched features for real image restoration and enhancement")

Question: Please tell me which class the source paper 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)