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: "multimodal hierarchical reinforcement learning policy for task oriented visual dialog"
  Abstract: Creating an intelligent conversational system that understands vision and language is one of the ultimate goals in Artificial Intelligence (AI)~\cite{winograd1972understanding}. Extensive research has focused on vision-to-language generation, however, limited research has touched on combining these two modalities in a goal-driven dialog context. We propose a multimodal hierarchical reinforcement learning framework that dynamically integrates vision and language for task-oriented visual dialog. The framework jointly learns the multimodal dialog state representation and the hierarchical dialog policy to improve both dialog task success and efficiency. We also propose a new technique, state adaptation, to integrate context awareness in the dialog state representation. We evaluate the proposed framework and the state adaptation technique in an image guessing game and achieve promising results.

Connected through hyperedge HE-1 (induced by source paper "hierarchical reinforcement learning for open domain dialog"):
  with members:
    - "do neural dialog systems use the conversation history effectively an empirical study"
    - "approximating interactive human evaluation with self play for open domain dialog systems"
    - "happybot generating empathetic dialogue responses by improving user experience look ahead"
    - "evaluating coherence in dialogue systems using entailment"
    - "universal adversarial triggers for attacking and analyzing nlp"
    - "what makes a good conversation how controllable attributes affect human judgments"
    - "deep reinforcement learning for modeling chit chat dialog with discrete attributes"
    - "way off policy batch deep reinforcement learning of implicit human preferences in dialog"

Connected through hyperedge HE-2 (induced by source paper "large scale answerer in questioner s mind for visual dialog question generation"):
  with members:
    - "unsupervised discrete sentence representation learning for interpretable neural dialog generation"
    - "visual coreference resolution in visual dialog using neural module networks"
    - "learning to ask good questions ranking clarification questions using neural expected value of perfect information"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "sliced recurrent neural networks"
    - "mine mutual information neural estimation"
    - "nsml meet the mlaas platform with a real world case study"
    - "two can play this game visual dialog with discriminative question generation and answering"

Connected through hyperedge HE-3 (induced by source paper "multi modal active learning from human data a deep reinforcement learning approach"):
  with members:
    - "personalized machine learning for robot perception of affect and engagement in autism therapy"
    - "multimodal machine translation with reinforcement learning"
    - "active one shot learning"
    - "multimodal machine learning a survey and taxonomy"
    - "cost effective active learning for deep image classification"
    - "learning how to active learn a deep reinforcement learning approach"
    - "learning active learning from data"
    - "rl 2 fast reinforcement learning via slow reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "building task oriented visual dialog systems through alternative optimization between dialog policy and language generation"):
  with members:
    - "rethinking action spaces for reinforcement learning in end to end dialog agents with latent variable models"
    - "sentiment adaptive end to end dialog systems"
    - "dialog based interactive image retrieval"
    - "evaluating visual conversational agents via cooperative human ai games"
    - "asking the difficult questions goal oriented visual question generation via intermediate rewards"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "deal or no deal end to end learning for negotiation dialogues"
    - "deep reinforcement learning for dialogue generation"

Connected through hyperedge HE-5 (induced by source paper "interactive semantic parsing for if then recipes via hierarchical reinforcement learning"):
  with members:
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "deep dyna q integrating planning for task completion dialogue policy learning"
    - "neural approaches to conversational ai"
    - "video captioning via hierarchical reinforcement learning"
    - "composite task completion dialogue policy learning via hierarchical deep reinforcement learning"
    - "composite task completion dialogue policy learning via hierarchical deep reinforcement learning"
    - "learning a neural semantic parser from user feedback"
    - "reasonet learning to stop reading in machine comprehension"

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)