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 "less is more picking informative frames for video captioning"):
  Abstract: In video captioning task, the best practice has been achieved by attention-based models which associate salient visual components with sentences in the video. However, existing study follows a common procedure which includes a frame-level appearance modeling and motion modeling on equal interval frame sampling, which may bring about redundant visual information, sensitivity to content noise and unnecessary computation cost. #R##N#We propose a plug-and-play PickNet to perform informative frame picking in video captioning. Based on a standard Encoder-Decoder framework, we develop a reinforcement-learning-based procedure to train the network sequentially, where the reward of each frame picking action is designed by maximizing visual diversity and minimizing textual discrepancy. If the candidate is rewarded, it will be selected and the corresponding latent representation of Encoder-Decoder will be updated for future trials. This procedure goes on until the end of the video sequence. Consequently, a compact frame subset can be selected to represent the visual information and perform video captioning without performance degradation. Experiment results shows that our model can use 6-8 frames to achieve competitive performance across popular benchmarks.

Includes members:
  Member "bidirectional attentive fusion with context gating for dense video captioning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "ha ccn hierarchical attention based crowd counting network")
  Member "reconstruction network for video captioning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "object relational graph with teacher recommended learning for video captioning")
  Member "supervising neural attention models for video captioning by human gaze data":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "attention flow end to end joint attention estimation")
  Member "weakly supervised dense video captioning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multi modal dense video captioning")
  Member "hierarchical boundary aware neural encoder for video captioning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multi modal dense video captioning")
  Member "to click or not to click automatic selection of beautiful thumbnails from videos":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "image to video domain adaptation using web supervision")
  Member "image captioning with semantic attention":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "aitpr attribute interaction tensor product representation for image caption")
  Member "hierarchical co attention for visual question answering":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning to reason end to end module networks for visual question answering")

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)