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: "deep dyna q integrating planning for task completion dialogue policy learning"
  Abstract: Training a task-completion dialogue agent via reinforcement learning (RL) is costly because it requires many interactions with real users. One common alternative is to use a user simulator. However, a user simulator usually lacks the language complexity of human interlocutors and the biases in its design may tend to degrade the agent. To address these issues, we present Deep Dyna-Q, which to our knowledge is the first deep RL framework that integrates planning for task-completion dialogue policy learning. We incorporate into the dialogue agent a model of the environment, referred to as the world model, to mimic real user response and generate simulated experience. During dialogue policy learning, the world model is constantly updated with real user experience to approach real user behavior, and in turn, the dialogue agent is optimized using both real experience and simulated experience. The effectiveness of our approach is demonstrated on a movie-ticket booking task in both simulated and human-in-the-loop settings.

Connected through hyperedge HE-1 (induced by source paper "self supervised dialogue learning"):
  with members:
    - "multi task deep neural networks for natural language understanding"
    - "self supervised learning for contextualized extractive summarization"
    - "cross lingual language model pretraining"
    - "adversarial learning of task oriented neural dialog models"
    - "deep contextualized word representations"
    - "neural approaches to conversational ai"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "discriminative deep dyna q robust planning for dialogue policy learning"

Connected through hyperedge HE-2 (induced by source paper "recsim a configurable simulation platform for recommender systems"):
  with members:
    - "reinforcement learning when all actions are not always available"
    - "advantage amplification in slowly evolving latent state environments"
    - "challenges of real world reinforcement learning"
    - "toward simulating environments in reinforcement learning based recommendations"
    - "dopamine a research framework for deep reinforcement learning"
    - "deep reinforcement learning for page wise recommendations"
    - "learning a deep listwise context model for ranking refinement"
    - "natural environment benchmarks for reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "towards end to end learning for efficient dialogue agent by modeling looking ahead ability"):
  with members:
    - "dialogue learning with human teaching and feedback in end to end trainable task oriented dialogue systems"
    - "multi task learning for joint language understanding and dialogue state tracking"
    - "composite task completion dialogue policy learning via hierarchical deep reinforcement learning"
    - "a survey on dialogue systems recent advances and new frontiers"
    - "composite task completion dialogue policy learning via hierarchical deep reinforcement learning"
    - "hybrid code networks practical and efficient end to end dialog control with supervised and reinforcement learning"
    - "deal or no deal end to end learning for negotiation dialogues"
    - "personalization in goal oriented dialog"

Connected through hyperedge HE-4 (induced by source paper "generating dialogue agents via automated planning"):
  with members:
    - "goal oriented chatbot dialog management bootstrapping with transfer learning"
    - "towards end to end reinforcement learning of dialogue agents for information access"

Connected through hyperedge HE-5 (induced by source paper "playing by the book an interactive game approach for action graph extraction from text"):
  with members:
    - "leveraging knowledge bases in lstms for improving machine reading"
    - "question answering as global reasoning over semantic abstractions"
    - "a survey of domain adaptation for neural machine translation"
    - "neural symbolic vqa disentangling reasoning from vision and language understanding"
    - "textworld a learning environment for text based games"
    - "building dynamic knowledge graphs from text using machine reading comprehension"
    - "learn what not to learn action elimination with deep reinforcement learning"
    - "open domain question answering using early fusion of knowledge bases and text"

Connected through hyperedge HE-6 (induced by source paper "interactive semantic parsing for if then recipes via hierarchical reinforcement learning"):
  with members:
    - "multimodal hierarchical reinforcement learning policy for task oriented visual dialog"
    - "seq2sql generating structured queries from natural language using reinforcement 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"

Connected through hyperedge HE-7 (induced by source paper "switch based active deep dyna q efficient adaptive planning for task completion dialogue policy learning"):
  with members:
    - "neural approaches to conversational ai"
    - "discriminative deep dyna q robust planning for dialogue policy learning"
    - "a tutorial on thompson sampling"
    - "end to end task completion neural dialogue systems"
    - "towards end to end reinforcement learning of dialogue agents for information access"
    - "a user simulator for task completion dialogues"
    - "continuously learning neural dialogue management"
    - "bbq networks efficient exploration in deep reinforcement learning for task oriented dialogue systems"

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)