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: "model selection for contextual bandits"
  Abstract: We introduce the problem of model selection for contextual bandits, wherein a learner must adapt to the complexity of the optimal policy while balancing exploration and exploitation. Our main result is a new model selection guarantee for linear contextual bandits. We work in the stochastic realizable setting with a sequence of nested linear policy classes of dimension $d_1 < d_2 < \ldots$, where the $m^\star$-th class contains the optimal policy, and we design an algorithm that achieves $\tilde{O}(T^{2/3}d^{1/3}_{m^\star})$ regret with no prior knowledge of the optimal dimension $d_{m^\star}$. The algorithm also achieves regret $\tilde{O}(T^{3/4} + \sqrt{Td_{m^\star}})$, which is optimal for $d_{m^{\star}}\geq{}\sqrt{T}$. This is the first contextual bandit model selection result with non-vacuous regret for all values of $d_{m^\star}$ and, to the best of our knowledge, is the first guarantee of its type in any contextual bandit setting. The core of the algorithm is a new estimator for the gap in best loss achievable by two linear policy classes, which we show admits a convergence rate faster than what is required to learn either class.

Connected through hyperedge HE-1 (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"
    - "contextual bandits with surrogate losses margin bounds and efficient algorithms"
    - "new insights into bootstrapping for bandits"
    - "practical contextual bandits with regression oracles"
    - "make the minority great again first order regret bound for contextual bandits"

Connected through hyperedge HE-2 (induced by source paper "efficient and robust algorithms for adversarial linear contextual bandits"):
  with members:
    - "weighted linear bandits for non stationary environments"
    - "comments on the du kakade wang yang lower bounds"
    - "is a good representation sufficient for sample efficient reinforcement learning"
    - "learning to optimize under non stationarity"
    - "iterate averaging as regularization for stochastic gradient descent"
    - "contextual bandits with surrogate losses margin bounds and efficient algorithms"
    - "make the minority great again first order regret bound for contextual bandits"
    - "kernel based methods for bandit convex optimization"

Connected through hyperedge HE-3 (induced by source paper "model selection in contextual stochastic bandit problems"):
  with members:
    - "a smoothed analysis of the greedy algorithm for the linear contextual bandit problem"
    - "corralling a band of bandit algorithms"
    - "an algorithm with nearly optimal pseudo regret for both stochastic and adversarial bandits"
    - "the best of both worlds stochastic and adversarial bandits"
    - "a contextual bandit approach to personalized news article recommendation"

Connected through hyperedge HE-4 (induced by source paper "sample complexity of reinforcement learning using linearly combined model ensembles"):
  with members:
    - "provably efficient reinforcement learning with linear function approximation"
    - "sample optimal parametric q learning using linearly additive features"
    - "reinforcement leaning in feature space matrix bandit kernels and regret bound"
    - "contextual markov decision processes using generalized linear models"
    - "model ensemble trust region policy optimization"
    - "learning dexterous in hand manipulation"
    - "sample efficient reinforcement learning with stochastic ensemble value expansion"
    - "model based rl in contextual decision processes pac bounds and exponential improvements over model free approaches"

Connected through hyperedge HE-5 (induced by source paper "hedging the drift learning to optimize under non stationarity"):
  with members:
    - "a new algorithm for non stationary contextual bandits efficient optimal and parameter free"
    - "meta dynamic pricing learning across experiments"
    - "reinforcement learning under drift"
    - "learning to optimize under non stationarity"
    - "provably optimal algorithms for generalized linear contextual bandits"
    - "tracking the best expert in non stationary stochastic environments"
    - "corralling a band of bandit algorithms"
    - "online optimization competing with dynamic comparators"

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)