human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "adversarial inference for multi sentence video description"):
  with members:
    - "video to video synthesis"
    - "grounding visual explanations"
    - "less is more picking informative frames for video captioning"
    - "learning a text video embedding from incomplete and heterogeneous data"
    - "improving reinforcement learning based image captioning with natural language prior"
    - "learning to write with cooperative discriminators"
    - "bidirectional attentive fusion with context gating for dense video captioning"
    - "move forward and tell a progressive generator of video descriptions"

Question: Please tell me which class the center node 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)