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: "perturbed history exploration in stochastic linear bandits"
  Abstract: We propose a new online algorithm for minimizing the cumulative regret in stochastic linear bandits. The key idea is to build a perturbed history, which mixes the history of observed rewards with a pseudo-history of randomly generated i.i.d. pseudo-rewards. Our algorithm, perturbed-history exploration in a linear bandit (LinPHE), estimates a linear model from its perturbed history and pulls the arm with the highest value under that model. We prove a $\tilde{O}(d \sqrt{n})$ gap-free bound on the expected $n$-round regret of LinPHE, where $d$ is the number of features. Our analysis relies on novel concentration and anti-concentration bounds on the weighted sum of Bernoulli random variables. To show the generality of our design, we extend LinPHE to a logistic reward model. We evaluate both algorithms empirically and show that they are practical.

Connected through hyperedge HE-1 (induced by source paper "old dog learns new tricks randomized ucb for bandit problems"):
  with members:
    - "empirical bayes regret minimization"
    - "randomized exploration in generalized linear bandits"
    - "garbage in reward out bootstrapping exploration in multi armed bandits"
    - "new insights into bootstrapping for bandits"
    - "provably optimal algorithms for generalized linear contextual bandits"
    - "a practical method for solving contextual bandit problems using decision trees"
    - "online stochastic linear optimization under one bit feedback"
    - "thompson sampling with the online bootstrap"

Connected through hyperedge HE-2 (induced by source paper "perturbed history exploration in stochastic multi armed bandits"):
  with members:
    - "provably optimal algorithms for generalized linear contextual bandits"
    - "a tutorial on thompson sampling"
    - "customized nonlinear bandits for online response selection in neural conversation models"
    - "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 "randomized exploration in generalized linear bandits"):
  with members:
    - "perturbed history exploration in stochastic multi armed bandits"
    - "provably optimal algorithms for generalized linear contextual bandits"
    - "a tutorial on thompson sampling"
    - "customized nonlinear bandits for online response selection in neural conversation models"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "garbage in reward out bootstrapping exploration in multi armed bandits"):
  with members:
    - "perturbed history exploration in stochastic multi armed bandits"
    - "new insights into bootstrapping for bandits"
    - "provably optimal algorithms for generalized linear contextual bandits"
    - "a practical method for solving contextual bandit problems using decision trees"
    - "online stochastic linear optimization under one bit feedback"
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "thompson sampling with the online bootstrap"
    - "api design for machine learning software experiences from the scikit learn project"

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)