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: "dialogue learning with human teaching and feedback in end to end trainable task oriented dialogue systems"
  Abstract: In this work, we present a hybrid learning method for training task-oriented dialogue systems through online user interactions. Popular methods for learning task-oriented dialogues include applying reinforcement learning with user feedback on supervised pre-training models. Efficiency of such learning method may suffer from the mismatch of dialogue state distribution between offline training and online interactive learning stages. To address this challenge, we propose a hybrid imitation and reinforcement learning method, with which a dialogue agent can effectively learn from its interaction with users by learning from human teaching and feedback. We design a neural network based task-oriented dialogue agent that can be optimized end-to-end with the proposed learning method. Experimental results show that our end-to-end dialogue agent can learn effectively from the mistake it makes via imitation learning from user teaching. Applying reinforcement learning with user feedback after the imitation learning stage further improves the agent's capability in successfully completing a task.

Connected through hyperedge HE-1 (induced by source paper "convert efficient and accurate conversational representations from transformers"):
  with members:
    - "deep retrieval based dialogue systems a short review"
    - "q8bert quantized 8bit bert"
    - "designing for health chatbots"
    - "are sixteen heads really better than one"
    - "dialog system technology challenge 7"
    - "distilbert a distilled version of bert smaller faster cheaper and lighter"
    - "multiwoz 2 1 multi domain dialogue state corrections and state tracking baselines"
    - "extreme language model compression with optimal subwords and shared projections"

Connected through hyperedge HE-2 (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-3 (induced by source paper "way off policy batch deep reinforcement learning of implicit human preferences in dialog"):
  with members:
    - "crossnorm normalization for off policy td reinforcement learning"
    - "off policy policy gradient with state distribution correction"
    - "approximating interactive human evaluation with self play for open domain dialog systems"
    - "off policy deep reinforcement learning by bootstrapping the covariate shift"
    - "happybot generating empathetic dialogue responses by improving user experience look ahead"
    - "learning from dialogue after deployment feed yourself chatbot"
    - "off policy deep reinforcement learning without exploration"
    - "the design and implementation of xiaoice an empathetic social chatbot"

Connected through hyperedge HE-4 (induced by source paper "survey on evaluation methods for dialogue systems"):
  with members:
    - "learning from dialogue after deployment feed yourself chatbot"
    - "the design and implementation of xiaoice an empathetic social chatbot"
    - "coqa a conversational question answering challenge"
    - "topic based evaluation for conversational bots"
    - "neural user simulation for corpus based policy optimisation for spoken dialogue systems"
    - "quac question answering in context"
    - "a hybrid convolutional variational autoencoder for text generation"
    - "a knowledge grounded neural conversation model"

Connected through hyperedge HE-5 (induced by source paper "dialogact2vec towards end to end dialogue agent by multi task representation learning"):
  with members:
    - "multi task learning for continuous control"
    - "towards universal dialogue state tracking"
    - "an end to end approach for handling unknown slot values in dialogue state tracking"
    - "global locally self attentive dialogue state tracker"
    - "multi task learning for joint language understanding and dialogue state tracking"
    - "learning personalized end to end goal oriented dialog"
    - "flexible and scalable state tracking framework for goal oriented dialogue systems"
    - "sequence to sequence learning for task oriented dialogue with dialogue state representation"

Connected through hyperedge HE-6 (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"
    - "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-7 (induced by source paper "learning from dialogue after deployment feed yourself chatbot"):
  with members:
    - "beyond turing intelligent agents centered on the user"
    - "bootstrapping conversational agents with weak supervision"
    - "training millions of personalized dialogue agents"
    - "a hierarchical multi task approach for learning embeddings from semantic tasks"
    - "learning to ask good questions ranking clarification questions using neural expected value of perfect information"
    - "interactive language acquisition with one shot visual concept learning through a conversational game"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "listen interact and talk learning to speak via interaction"

Connected through hyperedge HE-8 (induced by source paper "flexibly structured model for task oriented dialogues"):
  with members:
    - "microsoft dialogue challenge building end to end task completion dialogue systems"
    - "towards universal dialogue state tracking"
    - "an end to end approach for handling unknown slot values in dialogue state tracking"
    - "mem2seq effectively incorporating knowledge bases into end to end task oriented dialog systems"
    - "sequence to sequence learning for task oriented dialogue with dialogue state representation"
    - "large scale multi domain belief tracking with knowledge sharing"
    - "key value retrieval networks for task oriented dialogue"
    - "scalable multi domain dialogue state tracking"

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)