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 "deep image prior"):
  Abstract: Deep convolutional networks have become a popular tool for image generation and restoration. Generally, their excellent performance is imputed to their ability to learn realistic image priors from a large number of example images. In this paper, we show that, on the contrary, the structure of a generator network is sufficient to capture a great deal of low-level image statistics prior to any learning. In order to do so, we show that a randomly-initialized neural network can be used as a handcrafted prior with excellent results in standard inverse problems such as denoising, super-resolution, and inpainting. Furthermore, the same prior can be used to invert deep neural representations to diagnose them, and to restore images based on flash-no flash input pairs. #R##N#Apart from its diverse applications, our approach highlights the inductive bias captured by standard generator network architectures. It also bridges the gap between two very popular families of image restoration methods: learning-based methods using deep convolutional networks and learning-free methods based on handcrafted image priors such as self-similarity. Code and supplementary material are available at this https URL .

Includes members:
  Member "latent convolutional models":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "image processing using multi code gan prior")
  Member "phase retrieval for fourier ptychography under varying amount of measurements":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep ptych subsampled fourier ptychography using generative priors")
  Member "photorealistic image reconstruction from hybrid intensity and event based sensor":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "high frame rate video reconstruction based on an event camera")
  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 "convolutional dictionary learning via local processing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "orthogonal convolutional neural networks")
  Member "benchmarking denoising algorithms with real photographs":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "the robust manifold defense adversarial training using generative models":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "metrics and methods for robustness evaluation of neural networks with generative models")
  Member "zero shot super resolution using deep internal learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "restore from restored video restoration with pseudo clean video")

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)