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 "restore from restored video restoration with pseudo clean video"):
  Abstract: In this paper, we propose a self-supervised video denoising method called "restore-from-restored" that fine-tunes a baseline network by using a pseudo clean video at the test phase. The pseudo clean video can be obtained by applying an input noisy video to the pre-trained baseline network. By adopting a fully convolutional network (FCN) as the baseline, we can restore videos without accurate optical flow and registration due to its translation-invariant property unlike many conventional video restoration methods. Moreover, the proposed method can take advantage of the existence of many similar patches across consecutive frames (i.e., patch-recurrence), which can boost performance of the baseline network by a large margin. We analyze the restoration performance of the FCN fine-tuned with the proposed self-supervision-based training algorithm, and demonstrate that FCN can utilize recurring patches without the need for registration among adjacent frames. The proposed method can be applied to any FCN-based denoising models. In our experiments, we apply the proposed method to the state-of-the-art denoisers, and our results indicate a considerable improvementin task performance.

Includes members:
  Member "singan learning a generative model from a single natural image":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "meta transfer learning for zero shot super resolution")
  Member "residual non local attention networks for image restoration":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "noise2self blind denoising by self supervision":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "blur noise and compression robust generative adversarial networks")
  Member "real image denoising with feature attention":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "edvr video restoration with enhanced deformable convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "blurry video frame interpolation")
  Member "frame recurrent video super resolution":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "endol2h deep super resolution for capsule endoscopy")
  Member "residual dense network for image restoration":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "ffdnet toward a fast and flexible solution for cnn based image denoising":
    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)