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 "improving image captioning with conditional generative adversarial nets"):
  Abstract: In this paper, we propose a novel conditional generative adversarial nets based image captioning framework as an extension of traditional reinforcement learning (RL) based encoder-decoder architecture. To deal with the inconsistent evaluation problem between objective language metrics and subjective human judgements, we are inspired to design some "discriminator" networks to automatically and progressively determine whether generated caption is human described or machine generated. Two kinds of discriminator architecture (CNN and RNN based structures) are introduced since each has its own advantages. The proposed algorithm is generic so that it can enhance any existing encoder-decoder based image captioning model and we show that conventional RL training method is just a special case of our framework. Empirically, we show consistent improvements over all language evaluation metrics for different stage-of-the-art image captioning models.

Includes members:
  Member "context aware visual policy network for sequence level image captioning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deconfounded image captioning a causal retrospect")
  Member "recurrent fusion network for image captioning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deconfounded image captioning a causal retrospect")
  Member "improved image captioning via policy gradient optimization of spider":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "aitpr attribute interaction tensor product representation for image caption")
  Member "actor critic sequence training for image captioning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "aitpr attribute interaction tensor product representation for image caption")
  Member "deep reinforcement learning based image captioning with embedding reward":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "aitpr attribute interaction tensor product representation for image caption")
  Member "speaking the same language matching machine to human captions by adversarial training":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "say as you wish fine grained control of image caption generation with abstract scene graphs")
  Member "stack captioning coarse to fine learning for image captioning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "mutatt visual textual mutual guidance for referring expression comprehension")
  Member "bottom up and top down attention for image captioning and visual question answering":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "ha ccn hierarchical attention based crowd counting network")

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)