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 "sth spatio temporal hybrid convolution for efficient action recognition"):
  Abstract: Effective and Efficient spatio-temporal modeling is essential for action recognition. Existing methods suffer from the trade-off between model performance and model complexity. In this paper, we present a novel Spatio-Temporal Hybrid Convolution Network (denoted as "STH") which simultaneously encodes spatial and temporal video information with a small parameter cost. Different from existing works that sequentially or parallelly extract spatial and temporal information with different convolutional layers, we divide the input channels into multiple groups and interleave the spatial and temporal operations in one convolutional layer, which deeply incorporates spatial and temporal clues. Such a design enables efficient spatio-temporal modeling and maintains a small model scale. STH-Conv is a general building block, which can be plugged into existing 2D CNN architectures such as ResNet and MobileNet by replacing the conventional 2D-Conv blocks (2D convolutions). STH network achieves competitive or even better performance than its competitors on benchmark datasets such as Something-Something (V1 & V2), Jester, and HMDB-51. Moreover, STH enjoys performance superiority over 3D CNNs while maintaining an even smaller parameter cost than 2D CNNs.

Includes members:
  Member "stm spatiotemporal and motion encoding for action recognition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multi modal domain adaptation for fine grained action recognition")
  Member "dynamonet dynamic action and motion network":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning spatiotemporal features via video and text pair discrimination")
  Member "learning spatio temporal representation with local and global diffusion":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "ctm collaborative temporal modeling for action recognition")
  Member "eco efficient convolutional network for online video understanding":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "dynamic inference a new approach toward efficient video action recognition")
  Member "stnet local and global spatial temporal modeling for action recognition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "dynamic inference a new approach toward efficient video action recognition")
  Member "motion feature network fixed motion filter for action recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "dynamic inference a new approach toward efficient video action recognition")
  Member "videos as space time region graphs":
    also connected through other hyperedges:
      - Hyperedge HE-7 (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-8 (induced by source paper "semantic image networks for human 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)