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: "aiming to know you better perhaps makes me a more engaging dialogue partner"
  Abstract: There have been several attempts to define a plausible motivation for a chit-chat dialogue agent that can lead to engaging conversations. In this work, we explore a new direction where the agent specifically focuses on discovering information about its interlocutor. We formalize this approach by defining a quantitative metric. We propose an algorithm for the agent to maximize it. We validate the idea with human evaluation where our system outperforms various baselines. We demonstrate that the metric indeed correlates with the human judgments of engagingness.

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 "xpersona evaluating multilingual personalized chatbot"):
  with members:
    - "the second conversational intelligence challenge convai2"
    - "towards a human like open domain chatbot"
    - "named entity recognition on code switched data overview of the calcs 2018 shared task"
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "code switched language models using neural based synthetic data from parallel sentences"
    - "unsupervised cross lingual representation learning at scale"
    - "camembert a tasty french language model"
    - "zero shot cross lingual dialogue systems with transferable latent variables"

Connected through hyperedge HE-3 (induced by source paper "moel mixture of empathetic listeners"):
  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"
    - "happybot generating empathetic dialogue responses by improving user experience look ahead"
    - "deepcopy grounded response generation with hierarchical pointer networks"
    - "importance of search and evaluation strategies in neural dialogue modeling"
    - "skeleton to response dialogue generation guided by retrieval memory"
    - "retrieve and refine improved sequence generation models for dialogue"

Connected through hyperedge HE-4 (induced by source paper "multi turn beam search for neural dialogue modeling"):
  with members:
    - "model predictive policy learning with uncertainty regularization for driving in dense traffic"
    - "importance of search and evaluation strategies in neural dialogue modeling"
    - "modeling others using oneself in multi agent reinforcement learning"
    - "pyro deep universal probabilistic programming"
    - "autonomous agents modelling other agents a comprehensive survey and open problems"
    - "world models"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "importance of a search strategy in neural dialogue modelling"

Connected through hyperedge HE-5 (induced by source paper "getting to know you user attribute extraction from dialogues"):
  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"
    - "deepcopy grounded response generation with hierarchical pointer networks"
    - "transferable multi domain state generator for task oriented dialogue systems"
    - "retrieve and refine improved sequence generation models for dialogue"
    - "training millions of personalized dialogue agents"
    - "dialogue natural language inference"

Connected through hyperedge HE-6 (induced by source paper "personalizing dialogue agents via meta learning"):
  with members:
    - "the second conversational intelligence challenge convai2"
    - "global to local memory pointer networks for task oriented dialogue"
    - "importance of search and evaluation strategies in neural dialogue modeling"
    - "training millions of personalized dialogue agents"
    - "dialogue natural language inference"
    - "mem2seq effectively incorporating knowledge bases into end to end task oriented dialog systems"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "multi level memory for task oriented dialogs"

Connected through hyperedge HE-7 (induced by source paper "importance of search and evaluation strategies in neural dialogue modeling"):
  with members:
    - "the second conversational intelligence challenge convai2"
    - "dialogwae multimodal response generation with conditional wasserstein auto encoder"
    - "learning semantic textual similarity from conversations"
    - "retrieve and refine improved sequence generation models for dialogue"
    - "a stable and effective learning strategy for trainable greedy decoding"
    - "pyro deep universal probabilistic programming"
    - "training millions of personalized dialogue agents"
    - "personalizing dialogue agents i have a dog do you have pets too"

Connected through hyperedge HE-8 (induced by source paper "the rllchatbot a solution to the convai challenge"):
  with members:
    - "learning to ask neural question generation for reading comprehension"
    - "attention is all you need"
    - "a deep reinforcement learning chatbot"
    - "ethical challenges in data driven dialogue systems"
    - "deep reinforcement learning for dialogue generation"
    - "how not to evaluate your dialogue system an empirical study of unsupervised evaluation metrics for dialogue response generation"
    - "squad 100 000 questions for machine comprehension of text"
    - "on the evaluation of dialogue systems with next utterance classification"

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)