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: "integrating planning for task completion dialogue policy learning"
  Abstract: Training a task-completion dialogue agent with real users via reinforcement learning (RL) could be prohibitively expensive, because it requires many interactions with users. One alternative is to resort to a user simulator, while the discrepancy of between simulated and real users makes the learned policy unreliable in practice. This paper addresses these challenges by integrating planning into the dialogue policy learning based on Dyna-Q framework, and provides a more sample-efficient approach to learn the dialogue polices. The proposed agent consists of a planner trained on-line with limited real user experience that can generate large amounts of simulated experience to supplement with limited real user experience, and a policy model trained on these hybrid experiences. The effectiveness of our approach is validated on a movie-booking task in both a simulation setting and a human-in-the-loop setting.

Connected through hyperedge HE-1 (induced by source paper "recent advances and challenges in task oriented dialog system"):
  with members:
    - "convlab 2 an open source toolkit for building evaluating and diagnosing dialogue systems"
    - "crosswoz a large scale chinese cross domain task oriented dialogue dataset"
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "global to local memory pointer networks for task oriented dialogue"
    - "domain adaptive dialog generation via meta learning"
    - "meta learning for low resource natural language generation in task oriented dialogue systems"
    - "unsupervised dialog structure learning"
    - "cosql a conversational text to sql challenge towards cross domain natural language interfaces to databases"

Connected through hyperedge HE-2 (induced by source paper "learning reinforced agents with counterfactual simulation for medical automatic diagnosis"):
  with members:
    - "exploration by random network distillation"
    - "theoretical impediments to machine learning with seven sparks from the causal revolution"
    - "robustness to out of distribution inputs via task aware generative uncertainty"
    - "a survey on dialogue systems recent advances and new frontiers"
    - "towards end to end reinforcement learning of dialogue agents for information access"
    - "a user simulator for task completion dialogues"
    - "bbq networks efficient exploration in deep reinforcement learning for task oriented dialogue systems"
    - "the ubuntu dialogue corpus a large dataset for research in unstructured multi turn dialogue systems"

Connected through hyperedge HE-3 (induced by source paper "introducing mantis a novel multi domain information seeking dialogues dataset"):
  with members:
    - "simple applications of bert for ad hoc document retrieval"
    - "asking clarifying questions in open domain information seeking conversations"
    - "a hybrid retrieval generation neural conversation model"
    - "user intent prediction in information seeking conversations"
    - "interpretation of natural language rules in conversational machine reading"
    - "analyzing and characterizing user intent in information seeking conversations"
    - "subgoal discovery for hierarchical dialogue policy learning"
    - "learning to ask good questions ranking clarification questions using neural expected value of perfect information"

Connected through hyperedge HE-4 (induced by source paper "budgeted policy learning for task oriented dialogue systems"):
  with members:
    - "microsoft dialogue challenge building end to end task completion dialogue systems"
    - "dialogue learning with human teaching and feedback in end to end trainable task oriented dialogue systems"
    - "switch based active deep dyna q efficient adaptive planning for task completion dialogue policy learning"
    - "neural approaches to conversational ai"
    - "discriminative deep dyna q robust planning for dialogue policy learning"
    - "iterative policy learning in end to end trainable task oriented neural dialog models"
    - "a tutorial on thompson sampling"
    - "end to end task completion neural dialogue systems"

Connected through hyperedge HE-5 (induced by source paper "bridging dialogue generation and facial expression synthesis"):
  with members:
    - "emotionlines an emotion corpus of multi party conversations"
    - "dynamically context sensitive time decay attention for dialogue modeling"
    - "natural language generation by hierarchical decoding with linguistic patterns"
    - "a style based generator architecture for generative adversarial networks"
    - "ganimation anatomically aware facial animation from a single image"
    - "discriminative deep dyna q robust planning for dialogue policy learning"
    - "investigating linguistic pattern ordering in hierarchical natural language generation"
    - "progressive growing of gans for improved quality stability and variation"

Connected through hyperedge HE-6 (induced by source paper "model based reinforcement learning with adversarial training for online recommendation"):
  with members:
    - "model based reinforcement learning for whole chain recommendations"
    - "generative adversarial user model for reinforcement learning based recommendation system"
    - "top k off policy correction for a reinforce recommender system"
    - "value prediction network"
    - "constrained policy optimization"
    - "fast matrix factorization for online recommendation with implicit feedback"
    - "seqgan sequence generative adversarial nets with policy gradient"
    - "continuous deep q learning with model based acceleration"

Connected through hyperedge HE-7 (induced by source paper "dual supervised learning for natural language understanding and generation"):
  with members:
    - "dynamically context sensitive time decay attention for dialogue modeling"
    - "natural language generation by hierarchical decoding with linguistic patterns"
    - "discriminative deep dyna q robust planning for dialogue policy learning"
    - "investigating linguistic pattern ordering in hierarchical natural language generation"
    - "addressee and response selection in multi party conversations with speaker interaction rnns"
    - "dual supervised learning"
    - "listening while speaking speech chain by deep learning"
    - "dynamic time aware attention to speaker roles and contexts for spoken language understanding"

Connected through hyperedge HE-8 (induced by source paper "generative dialog policy for task oriented dialog systems"):
  with members:
    - "sample efficient actor critic reinforcement learning with supervised data for dialogue management"
    - "iterative policy learning in end to end trainable task oriented neural dialog models"
    - "frames a corpus for adding memory to goal oriented dialogue systems"
    - "a copy augmented sequence to sequence architecture gives good performance on task oriented dialogue"
    - "speaker role contextual modeling for language understanding and dialogue policy learning"
    - "an end to end trainable neural network model with belief tracking for task oriented dialog"
    - "a network based end to end trainable task oriented dialogue system"
    - "continuously learning neural dialogue management"

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)