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: "zero shot dialog generation with cross domain latent actions"
  Abstract: This paper introduces zero-shot dialog generation (ZSDG), as a step towards neural dialog systems that can instantly generalize to new situations with minimal data. ZSDG enables an end-to-end generative dialog system to generalize to a new domain for which only a domain description is provided and no training dialogs are available. Then a novel learning framework, Action Matching, is proposed. This algorithm can learn a cross-domain embedding space that models the semantics of dialog responses which, in turn, lets a neural dialog generation model generalize to new domains. We evaluate our methods on a new synthetic dialog dataset, and an existing human-human dialog dataset. Results show that our method has superior performance in learning dialog models that rapidly adapt their behavior to new domains and suggests promising future research.

Connected through hyperedge HE-1 (induced by source paper "learning to memorize in neural task oriented dialogue systems"):
  with members:
    - "transferable multi domain state generator for task oriented dialogue systems"
    - "skeleton to response dialogue generation guided by retrieval memory"
    - "the natural language decathlon multitask learning as question answering"
    - "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"
    - "emo2vec learning generalized emotion representation by multi task training"
    - "global locally self attentive dialogue state tracker"
    - "neural approaches to conversational ai"

Connected through hyperedge HE-2 (induced by source paper "structured fusion networks for dialog"):
  with members:
    - "the second conversational intelligence challenge convai2"
    - "pretraining methods for dialog context representation learning"
    - "rethinking action spaces for reinforcement learning in end to end dialog agents with latent variable models"
    - "semantically conditioned dialog response generation via hierarchical disentangled self attention"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "multiwoz a large scale multi domain wizard of oz dataset for task oriented dialogue modelling"
    - "cross domain dialogue policy transfer via simultaneous speech act and slot alignment"
    - "toward controlled generation of text"

Connected through hyperedge HE-3 (induced by source paper "mala cross domain dialogue generation with action learning"):
  with members:
    - "domain adaptive dialog generation via meta learning"
    - "carl aggregated search with context aware module embedding learning"
    - "rethinking action spaces for reinforcement learning in end to end dialog agents with latent variable models"
    - "semantically conditioned dialog response generation via hierarchical disentangled self attention"
    - "transferable multi domain state generator for task oriented dialogue systems"
    - "unsupervised text style transfer using language models as discriminators"
    - "unsupervised discrete sentence representation learning for interpretable neural dialog generation"
    - "mem2seq effectively incorporating knowledge bases into end to end task oriented dialog systems"

Connected through hyperedge HE-4 (induced by source paper "generating challenge datasets for task oriented conversational agents through self play"):
  with members:
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "building a conversational agent overnight with dialogue self play"
    - "key value retrieval networks for task oriented dialogue"
    - "learning end to end goal oriented dialog"
    - "a hierarchical latent variable encoder decoder model for generating dialogues"
    - "end to end memory networks"
    - "building end to end dialogue systems using generative hierarchical neural network models"
    - "evaluating prerequisite qualities for learning end to end dialog systems"

Connected through hyperedge HE-5 (induced by source paper "unsupervised dialog structure learning"):
  with members:
    - "unsupervised discrete sentence representation learning for interpretable neural dialog generation"
    - "quantized dialog language model for goal oriented conversational systems"
    - "sentiment adaptive end to end dialog systems"
    - "hybrid code networks practical and efficient end to end dialog control with supervised and reinforcement learning"
    - "discovering discrete latent topics with neural variational inference"
    - "learning discourse level diversity for neural dialog models using conditional variational autoencoders"
    - "a network based end to end trainable task oriented dialogue system"
    - "language as a latent variable discrete generative models for sentence compression"

Connected through hyperedge HE-6 (induced by source paper "a corpus free state2seq user simulator for task oriented dialogue"):
  with members:
    - "building a conversational agent overnight with dialogue self play"
    - "sequence to sequence data augmentation for dialogue language understanding"
    - "neural user simulation for corpus based policy optimisation for spoken dialogue systems"
    - "iterative policy learning in end to end trainable task oriented neural dialog models"
    - "end to end task completion neural dialogue systems"
    - "a user simulator for task completion dialogues"
    - "a sequence to sequence model for user simulation in spoken dialogue systems"

Connected through hyperedge HE-7 (induced by source paper "elastic crfs for open ontology slot filling"):
  with members:
    - "scalable multi domain dialogue state tracking"
    - "towards zero shot frame semantic parsing for domain scaling"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "domain adaptation of recurrent neural networks for natural language understanding"
    - "neural architectures for named entity recognition"
    - "structured prediction energy networks"
    - "multi domain dialog state tracking using recurrent neural networks"
    - "introduction to the conll 2003 shared task language independent named entity recognition"

Connected through hyperedge HE-8 (induced by source paper "joint on line learning of a zero shot spoken semantic parser and a reinforcement learning dialogue manager"):
  with members:
    - "towards zero shot frame semantic parsing for domain scaling"
    - "end to end task completion neural dialogue systems"
    - "towards end to end reinforcement learning of dialogue agents for information access"
    - "building end to end dialogue systems using generative hierarchical neural network models"
    - "efficient estimation of word representations in vector space"
    - "zero shot learning for semantic utterance classification"
    - "kalman temporal differences"

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)