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 "compression artifacts removal using convolutional neural networks"):
  Abstract: This paper shows that it is possible to train large and deep convolutional neural networks (CNN) for JPEG compression#N#artifacts reduction, and that such networks can provide significantly better reconstruction quality compared to#N#previously used smaller networks as well as to any other state-of-the-art methods. We were able to train networks#N#with 8 layers in a single step and in relatively short time by combining residual learning, skip architecture, and#N#symmetric weight initialization. We provide further insights into convolution networks for JPEG artifact reduction#N#by evaluating three different objectives, generalization with respect to training dataset size, and generalization with#N#respect to JPEG quality level.

Includes members:
  Member "cnn for license plate motion deblurring":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deblurring face images using uncertainty guided multi stream semantic networks")
  Member "accurate image super resolution using very deep convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "deep residual learning for image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "batch normalization accelerating deep network training by reducing internal covariate shift":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "compression artifacts reduction by a deep convolutional network":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "blur noise and compression robust generative adversarial networks")
  Member "learning a convolutional neural network for non uniform motion blur removal":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "blur noise and compression robust generative adversarial networks")
  Member "learning to deblur":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "blur noise and compression robust generative adversarial networks")
  Member "show and tell a neural image caption generator":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "relationship embedded representation learning for grounding referring expressions")

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)