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 "two stream flow guided convolutional attention networks for action recognition"):
  Abstract: This paper proposes a two-stream flow-guided convolutional attention networks for action recognition in videos. The central idea is that optical flows, when properly compensated for the camera motion, can be used to guide attention to the human foreground. We thus develop cross-link layers from the temporal network (trained on flows) to the spatial network (trained on RGB frames). These cross-link layers guide the spatial-stream to pay more attention to the human foreground areas and be less affected by background clutter. We obtain promising performances with our approach on the UCF101, HMDB51 and Hollywood2 datasets.

Includes members:
  Member "quo vadis action recognition a new model and the kinetics dataset":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "transformation based adversarial video prediction on large scale data")
  Member "temporal segment networks towards good practices for deep action recognition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "convolutional two stream network fusion for video action recognition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning spatiotemporal features via video and text pair discrimination")
  Member "spatiotemporal residual networks for video action recognition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "probabilistic future prediction for video scene understanding")
  Member "long term temporal convolutions for action recognition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning spatiotemporal features via video and text pair discrimination")
  Member "recurrent mixture density network for spatiotemporal visual attention":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "simultaneous enhancement and super resolution of underwater imagery for improved visual perception")
  Member "delving deeper into convolutional networks for learning video representations":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "transformation based adversarial video prediction on large scale data")
  Member "beyond short snippets deep networks for video classification":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning spatiotemporal features via video and text pair discrimination")

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)