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: "generative encoder decoder models for task oriented spoken dialog systems with chatting capability"
  Abstract: Generative encoder-decoder models offer great promise in developing domain-general dialog systems. However, they have mainly been applied to open-domain conversations. This paper presents a practical and novel framework for building task-oriented dialog systems based on encoder-decoder models. This framework enables encoder-decoder models to accomplish slot-value independent decision-making and interact with external databases. Moreover, this paper shows the flexibility of the proposed method by interleaving chatting capability with a slot-filling system for better out-of-domain recovery. The models were trained on both real-user data from a bus information system and human-human chat data. Results show that the proposed framework achieves good performance in both offline evaluation metrics and in task success rate with human users.

Connected through hyperedge HE-1 (induced by source paper "attention over parameters for dialogue systems"):
  with members:
    - "the second conversational intelligence challenge convai2"
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "global to local memory pointer networks for task oriented dialogue"
    - "moel mixture of empathetic listeners"
    - "deepcopy grounded response generation with hierarchical pointer networks"
    - "caire an end to end empathetic chatbot"
    - "learning from dialogue after deployment feed yourself chatbot"
    - "transferable multi domain state generator for task oriented dialogue systems"

Connected through hyperedge HE-2 (induced by source paper "hybrid generative retrieval transformers for dialogue domain adaptation"):
  with members:
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "relevance of unsupervised metrics in task oriented dialogue for evaluating natural language generation"
    - "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"
    - "challenging neural dialogue models with natural data memory networks fail on incremental phenomena"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

Connected through hyperedge HE-3 (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"
    - "zero shot dialog generation with cross domain latent actions"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "task oriented dialog systems that consider multiple appropriate responses under the same context"):
  with members:
    - "structured fusion networks for dialog"
    - "the curious case of neural text degeneration"
    - "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"
    - "semantically conditioned dialog response generation via hierarchical disentangled self attention"
    - "learning end to end goal oriented dialog with multiple answers"
    - "hierarchical neural story generation"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-5 (induced by source paper "multi granularity representations of dialog"):
  with members:
    - "the second conversational intelligence challenge convai2"
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "pretraining methods for dialog context representation learning"
    - "a bert baseline for the natural questions"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "end to end knowledge routed relational dialogue system for automatic diagnosis"):
  with members:
    - "scoutbot a dialogue system for collaborative navigation"
    - "mem2seq effectively incorporating knowledge bases into end to end task oriented dialog systems"
    - "key value retrieval networks for task oriented dialogue"
    - "video captioning via hierarchical reinforcement learning"
    - "end to end task completion neural dialogue systems"
    - "a copy augmented sequence to sequence architecture gives good performance on task oriented dialogue"
    - "augmenting end to end dialog systems with commonsense knowledge"
    - "how not to evaluate your dialogue system an empirical study of unsupervised evaluation metrics for dialogue response generation"

Connected through hyperedge HE-7 (induced by source paper "global to local memory pointer networks for task oriented dialogue"):
  with members:
    - "global locally self attentive dialogue state tracker"
    - "attention is all you need"
    - "a copy augmented sequence to sequence architecture gives good performance on task oriented dialogue"
    - "learning to attend copy and generate for session based query suggestion"
    - "natural language generation in dialogue using lexicalized and delexicalized data"
    - "memory enhanced decoder for neural machine translation"
    - "a network based end to end trainable task oriented dialogue system"
    - "key value memory networks for directly reading documents"

Connected through hyperedge HE-8 (induced by source paper "few shot classification in named entity recognition task"):
  with members:
    - "sequence labeling a practical approach"
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"
    - "label aware double transfer learning for cross specialty medical named entity recognition"
    - "learning how to self learn enhancing self training using neural reinforcement learning"
    - "neural cross lingual named entity recognition with minimal resources"
    - "deep active learning for named entity recognition"
    - "prototypical networks for few shot learning"

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)