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: "a neural networks committee for the contextual bandit problem"
  Abstract: This paper presents a new contextual bandit algorithm, NeuralBandit, which does not need hypothesis on stationarity of contexts and rewards. Several neural networks are trained to modelize the value of rewards knowing the context. Two variants, based on multi-experts approach, are proposed to choose online the parameters of multi-layer perceptrons. The proposed algorithms are successfully tested on a large dataset with and without stationarity of rewards.

Connected through hyperedge HE-1 (induced by source paper "neural contextual bandits with ucb based exploration"):
  with members:
    - "how much over parameterization is sufficient to learn deep relu networks"
    - "an improved analysis of training over parameterized deep neural networks"
    - "generalization error bounds of gradient descent for learning over parameterized deep relu networks"
    - "deep neural linear bandits overcoming catastrophic forgetting through likelihood matching"
    - "on exact computation with an infinitely wide neural net"
    - "reinforcement leaning in feature space matrix bandit kernels and regret bound"
    - "what can resnet learn efficiently going beyond kernels"
    - "generalization bounds of stochastic gradient descent for wide and deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "adaptive representation selection in contextual bandit with unlabeled history"):
  with members:
    - "online semi supervised learning with deep hybrid boltzmann machines and denoising autoencoders"
    - "thompson sampling for contextual bandits with linear payoffs"
    - "analysis of thompson sampling for the multi armed bandit problem"
    - "a contextual bandit approach to personalized news article recommendation"

Connected through hyperedge HE-3 (induced by source paper "a practical method for solving contextual bandit problems using decision trees"):
  with members:
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "thompson sampling with the online bootstrap"
    - "learning to optimize via posterior sampling"
    - "thompson sampling for contextual bandits with linear payoffs"
    - "efficient optimal learning for contextual bandits"
    - "a contextual bandit approach to personalized news article recommendation"

Connected through hyperedge HE-4 (induced by source paper "context attentive bandits contextual bandit with restricted context"):
  with members:
    - "recurrent models of visual attention"
    - "thompson sampling for contextual bandits with linear payoffs"
    - "a contextual bandit approach to personalized news article recommendation"

Connected through hyperedge HE-5 (induced by source paper "random forest for the contextual bandit problem extended version"):
  with members:
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "thompson sampling for contextual bandits with linear payoffs"
    - "a contextual bandit approach to personalized news article recommendation"

Connected through hyperedge HE-6 (induced by source paper "multi armed bandit problem with known trend"):
  with members:
    - "thompson sampling in switching environments with bayesian online change point detection"

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.NE(Neural and Evolutionary Computing)