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 "multimedia super resolution via deep learning a survey"):
  Abstract: Abstract   The recent phenomenal interest in convolutional neural networks (CNNs) must have made it inevitable for the super-resolution (SR) community to explore its potential. The response has been immense and in the last four years, since the advent of the pioneering work, there appeared too many works not to warrant a comprehensive survey. This paper surveys the SR literature in the context of deep learning. We focus on the three important aspects of multimedia – namely image, video and multi-dimensions, especially depth maps. In each case, first relevant benchmarks are introduced in the form of datasets and state of the art SR methods, excluding deep learning. Next is a detailed analysis of the individual works, each including a short description of the method and a critique of the results with special reference to the benchmarking done. This is followed by minimum overall benchmarking in the form of comparison on some common dataset, while relying on the results reported in various works.

Includes members:
  Member "joint convolutional neural pyramid for depth map super resolution":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "perceptual deep depth super resolution")
  Member "learning a mixture of deep networks for single image super resolution":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "mri super resolution with gan and 3d multi level densenet smaller faster and better")
  Member "an analysis of deep neural network models for practical applications":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "crl class representative learning for image classification")
  Member "deep laplacian pyramid networks for fast and accurate super resolution":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "perceptual image super resolution with progressive adversarial network")
  Member "failures of gradient based deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "regularizing activations in neural networks via distribution matching with the wasserstein metric")
  Member "enhanced deep residual networks for single image super resolution":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "fast and accurate image super resolution by deep cnn with skip connection and network in network":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learned multi view texture super resolution")
  Member "single image super resolution when model adaptation matters":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "single image super resolution via a lightweight residual convolutional neural network")

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)