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 "goal driven sequential data abstraction"):
  Abstract: Automatic data abstraction is an important capability for both benchmarking machine intelligence and supporting summarization applications. In the former one asks whether a machine can `understand' enough about the meaning of input data to produce a meaningful but more compact abstraction. In the latter this capability is exploited for saving space or human time by summarizing the essence of input data. In this paper we study a general reinforcement learning based framework for learning to abstract sequential data in a goal-driven way. The ability to define different abstraction goals uniquely allows different aspects of the input data to be preserved according to the ultimate purpose of the abstraction. Our reinforcement learning objective does not require human-defined examples of ideal abstraction. Importantly our model processes the input sequence holistically without being constrained by the original input order. Our framework is also domain agnostic -- we demonstrate applications to sketch, video and text data and achieve promising results in all domains.

Includes members:
  Member "learning deep sketch abstraction":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep self supervised representation learning for free hand sketch")
  Member "game of sketches deep recurrent models of pictionary style word guessing":
    also connected through other hyperedges:
      (none)
  Member "ranking sentences for extractive summarization with reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning by semantic similarity makes abstractive summarization better")
  Member "dtr gan dilated temporal relational adversarial network for video summarization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "comprehensive video understanding video summarization with content based video recommender design")
  Member "video summarisation by classification with deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "video summarization by learning from unpaired data")
  Member "discovering visual concept structure with sparse and incomplete tags":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "video summarisation by classification with deep reinforcement learning")
  Member "sequential dual deep learning with shape and texture features for sketch recognition":
    also connected through other hyperedges:
      (none)
  Member "neural extractive summarization with side information":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "what is this article about extreme summarization with topic aware convolutional neural 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)