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 "a deep journey into super resolution a survey"):
  Abstract: Deep convolutional networks based super-resolution is a fast-growing field with numerous practical applications. In this exposition, we extensively compare 30+ state-of-the-art super-resolution Convolutional Neural Networks (CNNs) over three classical and three recently introduced challenging datasets to benchmark single image super-resolution. We introduce a taxonomy for deep-learning based super-resolution networks that groups existing methods into nine categories including linear, residual, multi-branch, recursive, progressive, attention-based and adversarial designs. We also provide comparisons between the models in terms of network complexity, memory footprint, model input and output, learning details, the type of network losses and important architectural differences (e.g., depth, skip-connections, filters). The extensive evaluation performed, shows the consistent and rapid growth in the accuracy in the past few years along with a corresponding boost in model complexity and the availability of large-scale datasets. It is also observed that the pioneering methods identified as the benchmark have been significantly outperformed by the current contenders. Despite the progress in recent years, we identify several shortcomings of existing techniques and provide future research directions towards the solution of these open problems.

Includes members:
  Member "to learn image super resolution use a gan to learn how to do image degradation first":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "perceptual image super resolution with progressive adversarial network")
  Member "fast accurate and lightweight super resolution with cascading residual network":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "single image super resolution via cascaded multi scale cross network":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "channel splitting network for single mr image super resolution")
  Member "fast and accurate single image super resolution via information distillation network":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "the relativistic discriminator a key element missing from standard gan":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "the 2018 pirm challenge on perceptual image super resolution":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "perceptual image super resolution with progressive adversarial network")
  Member "deep underwater image enhancement":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "diving deeper into underwater image enhancement a survey")
  Member "image super resolution using very deep residual channel attention networks":
    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)