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: "optimally confident ucb improved regret for finite armed bandits"
  Abstract: I present the first algorithm for stochastic finite-armed bandits that simultaneously enjoys order-optimal problem-dependent regret and worst-case regret. Besides the theoretical results, the new algorithm is simple, efficient and empirically superb. The approach is based on UCB, but with a carefully chosen confidence parameter that optimally balances the risk of failing confidence intervals against the cost of excessive optimism.

Connected through hyperedge HE-1 (induced by source paper "accelerated learning from recommender systems using multi armed bandit"):
  with members:
    - "session based recommendation with graph neural network"
    - "productization challenges of contextual multi armed bandits"
    - "perturbed history exploration in stochastic multi armed bandits"
    - "on the difficulty of evaluating baselines a study on recommender systems"
    - "a batched multi armed bandit approach to news headline testing"
    - "a simple deep personalized recommendation system"
    - "a survey on session based recommender systems"
    - "multiplayer bandits without observing collision information"

Connected through hyperedge HE-2 (induced by source paper "distribution dependent and time uniform bounds for piecewise i i d bandits"):
  with members:
    - "a change detection based framework for piecewise stationary multi armed bandit problem"
    - "tracking the best expert in non stationary stochastic environments"
    - "thompson sampling in switching environments with bayesian online change point detection"

Connected through hyperedge HE-3 (induced by source paper "more adaptive algorithms for adversarial bandits"):
  with members:
    - "introduction to online convex optimization"
    - "an improved parametrization and analysis of the exp3 algorithm for stochastic and adversarial bandits"
    - "small loss bounds for online learning with partial information"
    - "sparsity variance and curvature in multi armed bandits"
    - "corralling a band of bandit algorithms"
    - "metagrad multiple learning rates in online learning"
    - "kernel based methods for bandit convex optimization"
    - "coin betting and parameter free online learning"

Connected through hyperedge HE-4 (induced by source paper "thompson sampling guided stochastic searching on the line for deceptive environments with applications to root finding problems"):
  with members:
    - "maximum a posteriori estimation by search in probabilistic programs"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "analysis of thompson sampling for the multi armed bandit problem"

Connected through hyperedge HE-5 (induced by source paper "efficient ucbv an almost optimal algorithm using variance estimates"):
  with members:
    - "thresholding bandits with augmented ucb"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "analysis of thompson sampling for the multi armed bandit problem"

Connected through hyperedge HE-6 (induced by source paper "regret analysis of the anytime optimally confident ucb algorithm"):
  with members:
    - "regret analysis of the finite horizon gittins index strategy for multi armed bandits"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"

Connected through hyperedge HE-7 (induced by source paper "the pareto regret frontier for bandits"):
  with members:
    - "on the prior sensitivity of thompson sampling"
    - "further optimal regret bounds for thompson sampling"
    - "analysis of thompson sampling for the multi armed bandit problem"
    - "prediction strategies without loss"
    - "adaptive online prediction by following the perturbed leader"

Connected through hyperedge HE-8 (induced by source paper "regret analysis of the finite horizon gittins index strategy for multi armed bandits"):
  with members:
    - "on the prior sensitivity of thompson sampling"
    - "the pareto regret frontier for bandits"
    - "further optimal regret bounds for thompson sampling"

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)