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 "blurry video frame interpolation"):
  Abstract: Existing works reduce motion blur and up-convert frame rate through two separate ways, including frame deblurring and frame interpolation. However, few studies have approached the joint video enhancement problem, namely synthesizing high-frame-rate clear results from low-frame-rate blurry inputs. In this paper, we propose a blurry video frame interpolation method to reduce motion blur and up-convert frame rate simultaneously. Specifically, we develop a pyramid module to cyclically synthesize clear intermediate frames. The pyramid module features adjustable spatial receptive field and temporal scope, thus contributing to controllable computational complexity and restoration ability. Besides, we propose an inter-pyramid recurrent module to connect sequential models to exploit the temporal relationship. The pyramid module integrates a recurrent module, thus can iteratively synthesize temporally smooth results without significantly increasing the model size. Extensive experimental results demonstrate that our method performs favorably against state-of-the-art methods.

Includes members:
  Member "unsupervised video interpolation using cycle consistency":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "softmax splatting for video frame interpolation")
  Member "depth aware video frame interpolation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "softmax splatting for video frame interpolation")
  Member "feedback network for image super resolution":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "feedback graph convolutional network for skeleton based action recognition")
  Member "refine and distill exploiting cycle inconsistency and knowledge distillation for unsupervised monocular depth estimation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "dreaming to distill data free knowledge transfer via deepinversion")
  Member "adversarial spatio temporal learning for video deblurring":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "spatiotemporal knowledge distillation for efficient estimation of aerial video saliency")
  Member "temporal cycle consistency learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "listen to look action recognition by previewing audio")
  Member "edvr video restoration with enhanced deformable convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "restore from restored video restoration with pseudo clean video")
  Member "video enhancement with task oriented flow":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "softmax splatting for video frame interpolation")

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)