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: "ask no more deciding when to guess in referential visual dialogue"
  Abstract: Our goal is to explore how the abilities brought in by a dialogue manager can be included in end-to-end visually grounded conversational agents. We make initial steps towards this general goal by augmenting a task-oriented visual dialogue model with a decision-making component that decides whether to ask a follow-up question to identify a target referent in an image, or to stop the conversation to make a guess. Our analyses show that adding a decision making component produces dialogues that are less repetitive and that include fewer unnecessary questions, thus potentially leading to more efficient and less unnatural interactions.

Connected through hyperedge HE-1 (induced by source paper "guessing state tracking for visual dialogue"):
  with members:
    - "clevr dialog a diagnostic dataset for multi round reasoning in visual dialog"
    - "visual dialogue state tracking for question generation"
    - "reasoning visual dialogs with structural and partial observations"
    - "making history matter history advantage sequence training for visual dialog"
    - "multi step reasoning via recurrent dual attention for visual dialog"
    - "answerer in questioner s mind information theoretic approach to goal oriented visual dialog"
    - "an active information seeking model for goal oriented vision and language tasks"
    - "beyond task success a closer look at jointly learning to see ask and guesswhat"

Connected through hyperedge HE-2 (induced by source paper "multi step reasoning via recurrent dual attention for visual dialog"):
  with members:
    - "image question answer synergistic network for visual dialog"
    - "reasoning visual dialogs with structural and partial observations"
    - "making history matter history advantage sequence training for visual dialog"
    - "dual attention networks for visual reference resolution in visual dialog"
    - "generative visual dialogue system via adaptive reasoning and weighted likelihood estimation"
    - "factor graph attention"
    - "relation aware graph attention network for visual question answering"
    - "flipdial a generative model for two way visual dialogue"

Connected through hyperedge HE-3 (induced by source paper "visual dialogue state tracking for question generation"):
  with members:
    - "transferable multi domain state generator for task oriented dialogue systems"
    - "answerer in questioner s mind information theoretic approach to goal oriented visual dialog"
    - "an active information seeking model for goal oriented vision and language tasks"
    - "beyond task success a closer look at jointly learning to see ask and guesswhat"
    - "evaluating visual conversational agents via cooperative human ai games"
    - "asking the difficult questions goal oriented visual question generation via intermediate rewards"
    - "image grounded conversations multimodal context for natural question and response generation"
    - "learning cooperative visual dialog agents with deep reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "beyond task success a closer look at jointly learning to see ask and guesswhat"):
  with members:
    - "interactive reinforcement learning for object grounding via self talking"
    - "asking the difficult questions goal oriented visual question generation via intermediate rewards"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "parallel attention a unified framework for visual object discovery through dialogs and queries"
    - "hybrid code networks practical and efficient end to end dialog control with supervised and reinforcement learning"
    - "end to end optimization of goal driven and visually grounded dialogue systems"
    - "deep reinforcement learning for dialogue generation"
    - "towards end to end learning for dialog state tracking and management using deep reinforcement learning"

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.CL(Computation and Language)