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: "a user simulator for task completion dialogues"
  Abstract: Despite widespread interests in reinforcement-learning for task-oriented dialogue systems, several obstacles can frustrate research and development progress. First, reinforcement learners typically require interaction with the environment, so conventional dialogue corpora cannot be used directly. Second, each task presents specific challenges, requiring separate corpus of task-specific annotated data. Third, collecting and annotating human-machine or human-human conversations for task-oriented dialogues requires extensive domain knowledge. Because building an appropriate dataset can be both financially costly and time-consuming, one popular approach is to build a user simulator based upon a corpus of example dialogues. Then, one can train reinforcement learning agents in an online fashion as they interact with the simulator. Dialogue agents trained on these simulators can serve as an effective starting point. Once agents master the simulator, they may be deployed in a real environment to interact with humans, and continue to be trained online. To ease empirical algorithmic comparisons in dialogues, this paper introduces a new, publicly available simulation framework, where our simulator, designed for the movie-booking domain, leverages both rules and collected data. The simulator supports two tasks: movie ticket booking and movie seeking. Finally, we demonstrate several agents and detail the procedure to add and test your own agent in the proposed framework.

Connected through hyperedge HE-1 (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"
    - "integrating planning for task completion dialogue policy learning"
    - "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"
    - "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-2 (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 dyna q integrating planning for task completion dialogue policy learning"
    - "deep contextualized word representations"
    - "neural approaches to conversational ai"
    - "bert pre training of deep bidirectional transformers for language understanding"

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:
    - "deep dyna q integrating planning for task completion dialogue policy learning"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "how to build user simulators to train rl based dialog systems"):
  with members:
    - "domain adaptive dialog generation via meta learning"
    - "unifying human and statistical evaluation for natural language generation"
    - "unsupervised dialog structure learning"
    - "convlab multi domain end to end dialog system platform"
    - "rethinking action spaces for reinforcement learning in end to end dialog agents with latent variable models"
    - "decoupling strategy and generation in negotiation dialogues"
    - "building a conversational agent overnight with dialogue self play"
    - "sentiment adaptive end to end dialog systems"

Connected through hyperedge HE-5 (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"
    - "integrating 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"

Connected through hyperedge HE-6 (induced by source paper "fast prototyping a dialogue comprehension system for nurse patient conversations on symptom monitoring"):
  with members:
    - "learning to write notes in electronic health records"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "building a conversational agent overnight with dialogue self play"
    - "know what you don t know unanswerable questions for squad"
    - "coqa a conversational question answering challenge"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "stochastic answer networks for squad 2 0"
    - "quac question answering in context"

Connected through hyperedge HE-7 (induced by source paper "towards personalized dialog policies for conversational skill discovery"):
  with members:
    - "the technological gap between virtual assistants and recommendation systems"
    - "continuous learning for large scale personalized domain classification"
    - "efficient large scale domain classification with personalized attention"
    - "dialogue learning with human teaching and feedback in end to end trainable task oriented dialogue systems"
    - "attentive memory networks efficient machine reading for conversational search"
    - "just ask building an architecture for extensible self service spoken language understanding"
    - "learning robust dialog policies in noisy environments"
    - "improving search through a3c reinforcement learning based conversational agent"

Connected through hyperedge HE-8 (induced by source paper "are you for real detecting identity fraud via dialogue interactions"):
  with members:
    - "incremental learning from scratch for task oriented dialogue systems"
    - "relational inductive biases deep learning and graph networks"
    - "a deep learning approach for multimodal deception detection"
    - "learning symmetric collaborative dialogue agents with dynamic knowledge graph embeddings"
    - "feudal networks for 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"
    - "end to end task completion neural 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.LG(Machine Learning)