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 "video modeling with correlation networks"):
  Abstract: Motion is a salient cue to recognize actions in video. Modern action recognition models leverage motion information either explicitly by using optical flow as input or implicitly by means of 3D convolutional filters that simultaneously capture appearance and motion information. This paper proposes an alternative approach based on a learnable correlation operator that can be used to establish frame-to-frame matches over convolutional feature maps in the different layers of the network. The proposed architecture enables the fusion of this explicit temporal matching information with traditional appearance cues captured by 2D convolution. Our correlation network compares favorably with widely-used 3D CNNs for video modeling, and achieves competitive results over the prominent two-stream network while being much faster to train. We empirically demonstrate that correlation networks produce strong results on a variety of video datasets, and outperform the state of the art on three popular benchmarks for action recognition: Kinetics, Something-Something and Diving48.

Includes members:
  Member "eco efficient convolutional network for online video understanding":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "dynamic inference a new approach toward efficient video action recognition")
  Member "end to end learning of motion representation for video understanding":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "dmc net generating discriminative motion cues for fast compressed video action recognition")
  Member "motion feature network fixed motion filter for action recognition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "dynamic inference a new approach toward efficient video action recognition")
  Member "using phase instead of optical flow for action recognition":
    also connected through other hyperedges:
      (none)
  Member "videos as space time region graphs":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "spatiotemporal relationship reasoning for pedestrian intent prediction")
  Member "spatio temporal channel correlation networks for action classification":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "semantic image networks for human action recognition")
  Member "representation flow for action recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "lightweight network architecture for real time action recognition")
  Member "learning discriminative motion features through detection":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "gate shift networks for video action recognition")

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)