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: "building a conversational agent overnight with dialogue self play"
  Abstract: We propose Machines Talking To Machines (M2M), a framework combining automation and crowdsourcing to rapidly bootstrap end-to-end dialogue agents for goal-oriented dialogues in arbitrary domains. M2M scales to new tasks with just a task schema and an API client from the dialogue system developer, but it is also customizable to cater to task-specific interactions. Compared to the Wizard-of-Oz approach for data collection, M2M achieves greater diversity and coverage of salient dialogue flows while maintaining the naturalness of individual utterances. In the first phase, a simulated user bot and a domain-agnostic system bot converse to exhaustively generate dialogue "outlines", i.e. sequences of template utterances and their semantic parses. In the second phase, crowd workers provide contextual rewrites of the dialogues to make the utterances more natural while preserving their meaning. The entire process can finish within a few hours. We propose a new corpus of 3,000 dialogues spanning 2 domains collected with M2M, and present comparisons with popular dialogue datasets on the quality and diversity of the surface forms and dialogue flows.

Connected through hyperedge HE-1 (induced by source paper "schema guided dialogue state tracking task at dstc8"):
  with members:
    - "fine tuning bert for schema guided zero shot dialogue state tracking"
    - "goal oriented multi task bert based dialogue state tracker"
    - "hyst a hybrid approach for flexible and accurate dialogue state tracking"
    - "multiwoz 2 1 multi domain dialogue state corrections and state tracking baselines"
    - "towards scalable multi domain conversational agents the schema guided dialogue dataset"
    - "robust zero shot cross domain slot filling with example values"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "transferable multi domain state generator for task oriented dialogue systems"

Connected through hyperedge HE-2 (induced by source paper "crosswoz a large scale chinese cross domain task oriented dialogue dataset"):
  with members:
    - "convlab 2 an open source toolkit for building evaluating and diagnosing dialogue systems"
    - "multiwoz 2 1 multi domain dialogue state corrections and state tracking baselines"
    - "towards scalable multi domain conversational agents the schema guided dialogue dataset"
    - "guided dialog policy learning reward estimation for multi domain task oriented dialog"
    - "pre training with whole word masking for chinese bert"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "key value retrieval networks for task oriented dialogue"
    - "composite task completion dialogue policy learning via hierarchical deep reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "fine tuning bert for schema guided zero shot dialogue state tracking"):
  with members:
    - "towards scalable multi domain conversational agents the schema guided dialogue dataset"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "zero shot adaptive transfer for conversational language understanding"
    - "multi task learning for joint language understanding and dialogue state tracking"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "towards zero shot frame semantic parsing for domain scaling"
    - "an end to end trainable neural network model with belief tracking for task oriented dialog"

Connected through hyperedge HE-4 (induced by source paper "submitting surveys via a conversational interface an evaluation of user acceptance and approach effectiveness"):
  with members:
    - "tell me about yourself using an ai powered chatbot to conduct conversational surveys"
    - "neural approaches to conversational ai"

Connected through hyperedge HE-5 (induced by source paper "don t paraphrase detect rapid and effective data collection for semantic parsing"):
  with members:
    - "learning to learn semantic parsers from natural language supervision"
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "end to end open domain question answering with"
    - "practical semantic parsing for spoken language understanding"
    - "building a neural semantic parser from a domain ontology"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "improving text to sql evaluation methodology"
    - "allennlp a deep semantic natural language processing platform"

Connected through hyperedge HE-6 (induced by source paper "self attentional models application in task oriented dialogue generation systems"):
  with members:
    - "universal transformers"
    - "tensor2tensor for neural machine translation"
    - "generating wikipedia by summarizing long sequences"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "conversational ai the science behind the alexa prize"
    - "key value retrieval networks for task oriented dialogue"
    - "attention is all you need"
    - "a survey on dialogue systems recent advances and new frontiers"

Connected through hyperedge HE-7 (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"
    - "sentiment adaptive end to end dialog systems"
    - "multiwoz a large scale multi domain wizard of oz dataset for task oriented dialogue modelling"

Connected through hyperedge HE-8 (induced by source paper "seeker real time interactive search"):
  with members:
    - "deep contextualized word representations"
    - "diversifying music recommendations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "web scale responsive visual search at bing"
    - "flowqa grasping flow in history for conversational machine comprehension"
    - "boltzmann exploration done right"
    - "a tutorial on thompson sampling"
    - "visual search at ebay"

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.AI(Artificial Intelligence)