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: "finite time analysis of kernelised contextual bandits"
  Abstract: We tackle the problem of online reward maximisation over a large finite set of actions described by their contexts. We focus on the case when the number of actions is too big to sample all of them even once. However we assume that we have access to the similarities between actions' contexts and that the expected reward is an arbitrary linear function of the contexts' images in the related reproducing kernel Hilbert space (RKHS). We propose KernelUCB, a kernelised UCB algorithm, and give a cumulative regret bound through a frequentist analysis. For contextual bandits, the related algorithm GP-UCB turns out to be a special case of our algorithm, and our finite-time analysis improves the regret bound of GP-UCB for the agnostic case, both in the terms of the kernel-dependent quantity and the RKHS norm of the reward function. Moreover, for the linear kernel, our regret bound matches the lower bound for contextual linear bandits.

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 "beyond ucb optimal and efficient contextual bandits with regression oracles"):
  with members:
    - "efficient and robust algorithms for adversarial linear contextual bandits"
    - "introduction to online convex optimization"
    - "comments on the du kakade wang yang lower bounds"
    - "is a good representation sufficient for sample efficient reinforcement learning"
    - "model selection for contextual bandits"
    - "contextual bandits with surrogate losses margin bounds and efficient algorithms"
    - "new insights into bootstrapping for bandits"
    - "practical contextual bandits with regression oracles"

Connected through hyperedge HE-3 (induced by source paper "self supervised contextual bandits in computer vision"):
  with members:
    - "scaling and benchmarking self supervised visual representation learning"
    - "exploiting unlabeled data in cnns by self supervised learning to rank"
    - "self supervised exploration via disagreement"
    - "boosting few shot visual learning with self supervision"
    - "semi supervised learning with scarce annotations"
    - "deep clustering for unsupervised learning of visual features"
    - "two stream self supervised learning for action recognition"
    - "large scale study of curiosity driven learning"

Connected through hyperedge HE-4 (induced by source paper "stochastic linear contextual bandits with diverse contexts"):
  with members:
    - "budget constrained edge service provisioning with demand estimation via bandit learning"
    - "a smoothed analysis of the greedy algorithm for the linear contextual bandit problem"
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "further optimal regret bounds for thompson sampling"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "efficient optimal learning for contextual bandits"
    - "analysis of thompson sampling for the multi armed bandit problem"
    - "a contextual bandit approach to personalized news article recommendation"

Connected through hyperedge HE-5 (induced by source paper "improved optimistic algorithms for logistic bandits"):
  with members:
    - "provably optimal algorithms for generalized linear contextual bandits"
    - "learning to optimize via posterior sampling"
    - "self concordant analysis for logistic regression"

Connected through hyperedge HE-6 (induced by source paper "neural contextual bandits with upper confidence bound based exploration"):
  with members:
    - "deep neural linear bandits overcoming catastrophic forgetting through likelihood matching"
    - "reinforcement leaning in feature space matrix bandit kernels and regret bound"
    - "generalization bounds of stochastic gradient descent for wide and deep neural networks"
    - "a generalization theory of gradient descent for learning over parameterized deep relu networks"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"
    - "neural tangent kernel convergence and generalization in neural networks"
    - "stochastic gradient descent optimizes over parameterized deep relu networks"
    - "a convergence theory for deep learning via over parameterization"

Connected through hyperedge HE-7 (induced by source paper "reinforcement leaning in feature space matrix bandit kernels and regret bound"):
  with members:
    - "sample optimal parametric q learning using linearly additive features"
    - "contextual markov decision processes using generalized linear models"
    - "sample optimal parametric q learning with linear transition models"
    - "is q learning provably efficient"
    - "policy certificates towards accountable reinforcement learning"
    - "online learning in kernelized markov decision processes"
    - "efficient exploration through bayesian deep q networks"
    - "on kernelized multi armed bandits"

Connected through hyperedge HE-8 (induced by source paper "adalinucb opportunistic learning for contextual bandits"):
  with members:
    - "adaptive exploration exploitation tradeoff for opportunistic bandits"
    - "unbiased offline evaluation of contextual bandit based news article recommendation algorithms"
    - "a contextual bandit approach to personalized news article recommendation"

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.LG(Machine Learning)