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: "customized nonlinear bandits for online response selection in neural conversation models"
  Abstract: Dialog response selection is an important step towards natural response generation in conversational agents. Existing work on neural conversational models mainly focuses on offline supervised learning using a large set of context-response pairs. In this paper, we focus on online learning of response selection in retrieval-based dialog systems. We propose a contextual multi-armed bandit model with a nonlinear reward function that uses distributed representation of text for online response selection. A bidirectional LSTM is used to produce the distributed representations of dialog context and responses, which serve as the input to a contextual bandit. In learning the bandit, we propose a customized Thompson sampling method that is applied to a polynomial feature space in approximating the reward. Experimental results on the Ubuntu Dialogue Corpus demonstrate significant performance gains of the proposed method over conventional linear contextual bandits. Moreover, we report encouraging response selection performance of the proposed neural bandit model using the Recall@k metric for a small set of online training samples.

Connected through hyperedge HE-1 (induced by source paper "a repository of conversational datasets"):
  with members:
    - "designing for health chatbots"
    - "training neural response selection for task oriented dialogue systems"
    - "learning semantic textual similarity from conversations"
    - "improving response selection in multi turn dialogue systems by incorporating domain knowledge"
    - "fully statistical neural belief tracking"
    - "microsoft dialogue challenge building end to end task completion dialogue systems"
    - "deep contextualized word representations"
    - "universal sentence encoder"

Connected through hyperedge HE-2 (induced by source paper "perturbed history exploration in stochastic multi armed bandits"):
  with members:
    - "perturbed history exploration in stochastic linear bandits"
    - "provably optimal algorithms for generalized linear contextual bandits"
    - "a tutorial on thompson sampling"
    - "dcm bandits learning to rank with multiple clicks"
    - "bbq networks efficient exploration in deep reinforcement learning for task oriented dialogue systems"
    - "cascading bandits learning to rank in the cascade model"
    - "online stochastic linear optimization under one bit feedback"
    - "further optimal regret bounds for thompson sampling"

Connected through hyperedge HE-3 (induced by source paper "perturbed history exploration in stochastic linear bandits"):
  with members:
    - "new insights into bootstrapping for bandits"
    - "provably optimal algorithms for generalized linear contextual bandits"
    - "a tutorial on thompson sampling"
    - "a practical method for solving contextual bandit problems using decision trees"
    - "thompson sampling with the online bootstrap"
    - "further optimal regret bounds for thompson sampling"
    - "thompson sampling for contextual bandits with linear payoffs"

Connected through hyperedge HE-4 (induced by source paper "randomized exploration in generalized linear bandits"):
  with members:
    - "perturbed history exploration in stochastic multi armed bandits"
    - "perturbed history exploration in stochastic linear bandits"
    - "provably optimal algorithms for generalized linear contextual bandits"
    - "a tutorial on thompson sampling"
    - "bbq networks efficient exploration in deep reinforcement learning for task oriented dialogue systems"
    - "further optimal regret bounds for thompson sampling"
    - "thompson sampling for contextual bandits with linear payoffs"

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)