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: "stalematebreaker a proactive content introducing approach to automatic human computer conversation"
  Abstract: Existing open-domain human-computer conversation systems are typically passive: they either synthesize or retrieve a reply provided a human-issued utterance. It is generally presumed that humans should take the role to lead the conversation and introduce new content when a stalemate occurs, and that the computer only needs to "respond." In this paper, we propose StalemateBreaker, a conversation system that can proactively introduce new content when appropriate. We design a pipeline to determine when, what, and how to introduce new content during human-computer conversation. We further propose a novel reranking algorithm Bi-PageRank-HITS to enable rich interaction between conversation context and candidate replies. Experiments show that both the content-introducing approach and the reranking algorithm are effective. Our full StalemateBreaker model outperforms a state-of-the-practice conversation system by +14.4% p@1 when a stalemate occurs.

Connected through hyperedge HE-1 (induced by source paper "learning to customize language model for generation based dialog systems"):
  with members:
    - "domain adaptive dialog generation via meta learning"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "meta transfer learning for few shot learning"
    - "attention is all you need"
    - "prototypical networks for few shot learning"
    - "learning to compare relation network for few shot learning"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "smash one shot model architecture search through hypernetworks"

Connected through hyperedge HE-2 (induced by source paper "fine grained sentence functions for short text conversation"):
  with members:
    - "sentiment adaptive end to end dialog systems"
    - "emotional chatting machine emotional conversation generation with internal and external memory"
    - "controlling linguistic style aspects in neural language generation"
    - "steering output style and topic in neural response generation"
    - "learning discourse level diversity for neural dialog models using conditional variational autoencoders"
    - "how not to evaluate your dialogue system an empirical study of unsupervised evaluation metrics for dialogue response generation"
    - "topic aware neural response generation"
    - "convolutional neural network architectures for matching natural language sentences"

Connected through hyperedge HE-3 (induced by source paper "from eliza to xiaoice challenges and opportunities with social chatbots"):
  with members:
    - "annotating and modeling empathy in spoken conversations"
    - "a persona based neural conversation model"
    - "towards empathetic human robot interactions"
    - "a network based end to end trainable task oriented dialogue system"
    - "multiresolution recurrent neural networks an application to dialogue response generation"
    - "wavenet a generative model for raw audio"
    - "achieving human parity in conversational speech recognition"
    - "senticap generating image descriptions with sentiments"

Connected through hyperedge HE-4 (induced by source paper "rubystar a non task oriented mixture model dialog system"):
  with members:
    - "just ask building an architecture for extensible self service spoken language understanding"
    - "opennmt open source toolkit for neural machine translation"
    - "adversarial learning for neural dialogue generation"
    - "a persona based neural conversation model"
    - "deep reinforcement learning for dialogue generation"
    - "how not to evaluate your dialogue system an empirical study of unsupervised evaluation metrics for dialogue response generation"
    - "two are better than one an ensemble of retrieval and generation based dialog systems"
    - "building end to end dialogue systems using generative hierarchical neural network models"

Connected through hyperedge HE-5 (induced by source paper "two are better than one an ensemble of retrieval and generation based dialog systems"):
  with members:
    - "sequence to backward and forward sequences a content introducing approach to generative short text conversation"
    - "a network based end to end trainable task oriented dialogue system"
    - "how not to evaluate your dialogue system an empirical study of unsupervised evaluation metrics for dialogue response generation"
    - "topic augmented neural response generation with a joint attention mechanism"
    - "continuously learning neural dialogue management"
    - "multi source neural translation"
    - "a hierarchical latent variable encoder decoder model for generating dialogues"
    - "a neural attention model for abstractive sentence summarization"

Connected through hyperedge HE-6 (induced by source paper "sequence to backward and forward sequences a content introducing approach to generative short text conversation"):
  with members:
    - "topic augmented neural response generation with a joint attention mechanism"
    - "chinese poetry generation with planning based neural network"
    - "a hierarchical latent variable encoder decoder model for generating dialogues"
    - "a neural attention model for abstractive sentence summarization"
    - "backward and forward language modeling for constrained sentence generation"
    - "building end to end dialogue systems using generative hierarchical neural network models"
    - "neural generative question answering"
    - "a neural network approach to context sensitive generation of conversational responses"

Connected through hyperedge HE-7 (induced by source paper "incorporating loose structured knowledge into conversation modeling via recall gate lstm"):
  with members:
    - "end to end memory networks"
    - "the influence of context on dialogue act recognition"
    - "improved deep learning baselines for ubuntu corpus dialogs"
    - "building end to end dialogue systems using generative hierarchical neural network models"
    - "a survey of available corpora for building data driven dialogue systems"
    - "the ubuntu dialogue corpus a large dataset for research in unstructured multi turn dialogue systems"
    - "a neural network approach to context sensitive generation of conversational responses"
    - "neural responding machine for short text conversation"

Connected through hyperedge HE-8 (induced by source paper "dialogue session segmentation by embedding enhanced texttiling"):
  with members:
    - "a hierarchical recurrent encoder decoder for generative context aware query suggestion"
    - "backward and forward language modeling for constrained sentence generation"
    - "building end to end dialogue systems using generative hierarchical neural network models"
    - "natural language inference by tree based convolution and heuristic matching"
    - "a neural network approach to context sensitive generation of conversational responses"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"
    - "topic segmentation and labeling in asynchronous conversations"

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)