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 "first image then video a two stage network for spatiotemporal video denoising"):
  Abstract: Video denoising is to remove noise from noise-corrupted data, thus recovering true signals via spatiotemporal processing. Existing approaches for spatiotemporal video denoising tend to suffer from motion blur artifacts, that is, the boundary of a moving object tends to appear blurry especially when the object undergoes a fast motion, causing optical flow calculation to break down. In this paper, we address this challenge by designing a first-image-then-video two-stage denoising neural network, consisting of an image denoising module for spatially reducing intra-frame noise followed by a regular spatiotemporal video denoising module. The intuition is simple yet powerful and effective: the first stage of image denoising effectively reduces the noise level and, therefore, allows the second stage of spatiotemporal denoising for better modeling and learning everywhere, including along the moving object boundaries. This two-stage network, when trained in an end-to-end fashion, yields the state-of-the-art performances on the video denoising benchmark Vimeo90K dataset in terms of both denoising quality and computation. It also enables an unsupervised approach that achieves comparable performance to existing supervised approaches.

Includes members:
  Member "deep video inpainting":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "motion excited sampler video adversarial attack with sparked prior")
  Member "noisy as clean learning unsupervised denoising from the corrupted image":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "semi supervised self growing generative adversarial networks for image recognition")
  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 "videnn deep blind video denoising":
    also connected through other hyperedges:
      (none)
  Member "fastdvdnet towards real time video denoising without explicit motion estimation":
    also connected through other hyperedges:
      (none)
  Member "noise2void learning denoising from single noisy images":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "blur noise and compression robust generative adversarial networks")
  Member "noise2noise learning image restoration without clean data":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "transfer learning from synthetic to real noise denoising with adaptive instance normalization")
  Member "non local video denoising by cnn":
    also connected through other hyperedges:
      - Hyperedge HE-6 (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)