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 "stage spatio temporal attention on graph entities for video action detection"):
  Abstract: Spatio-temporal action localization is a challenging yet fascinating task that aims to detect and classify human actions in video clips. In this paper, we develop a high-level video understanding module which can encode interactions between actors and objects both in space and time. In our formulation, spatio-temporal relationships are learned by performing self-attention operations on a graph structure connecting entities from consecutive clips. Noticeably, the use of graph learning is unprecedented for this task. From a computational point of view, the proposed module is backbone independent by design and does not need end-to-end training. When tested on the AVA dataset, it demonstrates a 10-16% relative mAP improvement over the baseline. Further, it can outperform or bring performances comparable to state-of-the-art models which require heavy end-to-end and synchronized training on multiple GPUs. Code is publicly available at: this https URL.

Includes members:
  Member "step spatio temporal progressive learning for video action detection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "actions as moving points")
  Member "collaborative spatio temporal feature learning for video action recognition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning efficient video representation with video shuffle networks")
  Member "actor conditioned attention maps for video action detection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "vsgnet spatial attention network for detecting human object interactions using graph convolutions")
  Member "long term feature banks for detailed video understanding":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "mast a memory augmented self supervised tracker")
  Member "a better baseline for ava":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "action genome actions as composition of spatio temporal scene graphs")
  Member "actor centric relation network":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "symbiotic attention with privileged information for egocentric action recognition")
  Member "video action transformer network":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "pic permutation invariant convolution for recognizing long range activities")
  Member "timeception for complex action recognition":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "ego topo environment affordances from egocentric 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)