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 "a self adaptive proposal model for temporal action detection based on reinforcement learning"):
  Abstract: Existing action detection algorithms usually generate action proposals through an extensive search over the video at multiple temporal scales, which brings about huge computational overhead and deviates from the human perception procedure. We argue that the process of detecting actions should be naturally one of observation and refinement: observe the current window and refine the span of attended window to cover true action regions. In this paper, we propose an active action proposal model that learns to find actions through continuously adjusting the temporal bounds in a self-adaptive way. The whole process can be deemed as an agent, which is firstly placed at a position in the video at random, adopts a sequence of transformations on the current attended region to discover actions according to a learned policy. We utilize reinforcement learning, especially the Deep Q-learning algorithm to learn the agent's decision policy. In addition, we use temporal pooling operation to extract more effective feature representation for the long temporal window, and design a regression network to adjust the position offsets between predicted results and the ground truth. Experiment results on THUMOS 2014 validate the effectiveness of the proposed approach, which can achieve competitive performance with current action detection algorithms via much fewer proposals.

Includes members:
  Member "turn tap temporal unit regression network for temporal action proposals":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "constraining temporal relationship for action localization")
  Member "tree structured reinforcement learning for sequential object localization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "pixelrl fully convolutional network with reinforcement learning for image processing")
  Member "a pursuit of temporal accuracy in general activity detection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "constraining temporal relationship for action localization")
  Member "temporal action localization in untrimmed videos via multi stage cnns":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "weakly supervised temporal action localization using deep metric learning")
  Member "efficient action detection in untrimmed videos via multi task learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "skeleton based online action prediction using scale selection network")
  Member "hierarchical object detection with deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "robust multimodal image registration using deep recurrent reinforcement learning")
  Member "end to end learning of action detection from frame glimpses in videos":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "video storytelling textual summaries for events")
  Member "faster r cnn towards real time object detection with region proposal networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")

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)