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: "optimal exploration exploitation in a multi armed bandit problem with non stationary rewards"
  Abstract: In a multi-armed bandit (MAB) problem a gambler needs to choose at each round of play one of K arms, each characterized by an unknown reward distribution. Reward realizations are only observed when an arm is selected, and the gambler's objective is to maximize his cumulative expected earnings over some given horizon of play T. To do this, the gambler needs to acquire information about arms (exploration) while simultaneously optimizing immediate rewards (exploitation); the price paid due to this trade off is often referred to as the regret, and the main question is how small can this price be as a function of the horizon length T. This problem has been studied extensively when the reward distributions do not change over time; an assumption that supports a sharp characterization of the regret, yet is often violated in practical settings. In this paper, we focus on a MAB formulation which allows for a broad range of temporal uncertainties in the rewards, while still maintaining mathematical tractability. We fully characterize the (regret) complexity of this class of MAB problems by establishing a direct link between the extent of allowable reward "variation" and the minimal achievable regret. Our analysis draws some connections between two rather disparate strands of literature: the adversarial and the stochastic MAB frameworks.

Connected through hyperedge HE-1 (induced by source paper "regime switching bandits"):
  with members:
    - "a survey on practical applications of multi armed and contextual bandits"
    - "restless hidden markov bandits with linear rewards"
    - "introduction to multi armed bandits"
    - "learning to optimize under non stationarity"
    - "multi armed bandits for correlated markovian environments with smoothed reward feedback"
    - "exploration bonus for regret minimization in undiscounted discrete and continuous markov decision processes"
    - "reinforcement learning of pomdp s using spectral methods"
    - "reinforcement learning of pomdps using spectral methods"

Connected through hyperedge HE-2 (induced by source paper "weighted linear bandits for non stationary environments"):
  with members:
    - "a new algorithm for non stationary contextual bandits efficient optimal and parameter free"
    - "learning to optimize under non stationarity"
    - "learning contextual bandits in a non stationary environment"
    - "hedging the drift learning to optimize under non stationarity"
    - "efficient contextual bandits in non stationary worlds"
    - "a contextual bandit approach to personalized news article recommendation"

Connected through hyperedge HE-3 (induced by source paper "multi armed bandit strategies for non stationary reward distributions and delayed feedback processes"):
  with members:
    - "algorithms for multi armed bandit problems"
    - "analysis of thompson sampling for the multi armed bandit problem"

Connected through hyperedge HE-4 (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"
    - "model selection for contextual bandits"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "adaptive sequential experiments with unknown information flows"):
  with members:
    - "meta dynamic pricing learning across experiments"
    - "bandit learning with positive externalities"
    - "estimating the causal impact of recommendation systems from observational data"
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "real time bidding benchmarking with ipinyou dataset"
    - "empirical analysis of predictive algorithms for collaborative filtering"
    - "real time bid optimization with smooth budget delivery in online advertising"
    - "further optimal regret bounds for thompson sampling"

Connected through hyperedge HE-6 (induced by source paper "learning to optimize under non stationarity"):
  with members:
    - "tracking the best expert in non stationary stochastic environments"
    - "corralling a band of bandit algorithms"
    - "online optimization competing with dynamic comparators"
    - "learning to optimize via posterior sampling"
    - "thompson sampling for contextual bandits with linear payoffs"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "linearly parameterized bandits"

Connected through hyperedge HE-7 (induced by source paper "online influence maximization in non stationary social networks"):
  with members:
    - "online influence maximization"
    - "an online approach to dynamic channel access and transmission scheduling"
    - "online learning for combinatorial network optimization with restless markovian rewards"

Connected through hyperedge HE-8 (induced by source paper "an online approach to dynamic channel access and transmission scheduling"):
  with members:
    - "sufficient conditions on the optimality of myopic sensing in opportunistic channel access a unifying framework"
    - "jamming resistant learning in wireless networks"
    - "to stay or to switch multiuser dynamic channel access"
    - "performance and convergence of multi user online learning"
    - "on a game theoretic approach to capacity maximization in wireless networks"

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)