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: "a new algorithm for non stationary contextual bandits efficient optimal and parameter free"
  Abstract: We propose the first contextual bandit algorithm that is parameter-free, efficient, and optimal in terms of dynamic regret. Specifically, our algorithm achieves dynamic regret $\mathcal{O}(\min\{\sqrt{ST}, \Delta^{\frac{1}{3}}T^{\frac{2}{3}}\})$ for a contextual bandit problem with $T$ rounds, $S$ switches and $\Delta$ total variation in data distributions. Importantly, our algorithm is adaptive and does not need to know $S$ or $\Delta$ ahead of time, and can be implemented efficiently assuming access to an ERM oracle. #R##N#Our results strictly improve the $\mathcal{O}(\min \{S^{\frac{1}{4}}T^{\frac{3}{4}}, \Delta^{\frac{1}{5}}T^{\frac{4}{5}}\})$ bound of (Luo et al., 2018), and greatly generalize and improve the $\mathcal{O}(\sqrt{ST})$ result of (Auer et al, 2018) that holds only for the two-armed bandit problem without contextual information. The key novelty of our algorithm is to introduce replay phases, in which the algorithm acts according to its previous decisions for a certain amount of time in order to detect non-stationarity while maintaining a good balance between exploration and exploitation.

Connected through hyperedge HE-1 (induced by source paper "combinatorial semi bandit in the non stationary environment"):
  with members:
    - "weighted linear bandits for non stationary environments"
    - "online second price auction with semi bandit feedback under the non stationary setting"
    - "beating stochastic and adversarial semi bandits optimally and simultaneously"
    - "be aware of non stationarity nearly optimal algorithms for piecewise stationary cascading bandits"
    - "a near optimal change detection based algorithm for piecewise stationary combinatorial semi bandits"
    - "learning to optimize under non stationarity"
    - "improving regret bounds for combinatorial semi bandits with probabilistically triggered arms and its applications"
    - "a change detection based framework for piecewise stationary multi armed bandit problem"

Connected through hyperedge HE-2 (induced by source paper "taking a hint how to leverage loss predictors in contextual bandits"):
  with members:
    - "improved path length regret bounds for bandits"
    - "more adaptive algorithms for adversarial bandits"
    - "make the minority great again first order regret bound for contextual bandits"
    - "tracking the best expert in non stationary stochastic environments"
    - "improved regret bounds for oracle based adversarial contextual bandits"
    - "efficient algorithms for adversarial contextual learning"
    - "fast convergence of regularized learning in games"
    - "strongly adaptive online learning"

Connected through hyperedge HE-3 (induced by source paper "parameter free learning for evolving markov decision processes the blessing of more optimism"):
  with members:
    - "variational regret bounds for reinforcement learning"
    - "learning in structured mdps with convex cost functions improved regret bounds for inventory management"
    - "large scale markov decision processes with changing rewards"
    - "a sliding window algorithm for markov decision processes with arbitrarily changing rewards and transitions"
    - "learning to optimize under non stationarity"
    - "efficient bias span constrained exploration exploitation in reinforcement learning"
    - "hedging the drift learning to optimize under non stationarity"
    - "real time bidding by reinforcement learning in display advertising"

Connected through hyperedge HE-4 (induced by source paper "model selection for contextual bandits"):
  with members:
    - "estimating learnability in the sublinear data regime"
    - "learning to optimize under non stationarity"
    - "a smoothed analysis of the greedy algorithm for the linear contextual bandit problem"
    - "the externalities of exploration and how data diversity helps exploitation"
    - "practical contextual bandits with regression oracles"
    - "make the minority great again first order regret bound for contextual bandits"
    - "provably optimal algorithms for generalized linear contextual bandits"
    - "small loss bounds for online learning with partial information"

Connected through hyperedge HE-5 (induced by source paper "weighted linear bandits for non stationary environments"):
  with members:
    - "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"
    - "optimal exploration exploitation in a multi armed bandit problem with non stationary rewards"
    - "a contextual bandit approach to personalized news article recommendation"

Connected through hyperedge HE-6 (induced by source paper "variational regret bounds for reinforcement learning"):
  with members:
    - "selecting near optimal approximate state representations in reinforcement learning"
    - "regret bounds for restless markov bandits"
    - "regal a regularization based algorithm for reinforcement learning in weakly communicating mdps"

Connected through hyperedge HE-7 (induced by source paper "hedging the drift learning to optimize under non stationarity"):
  with members:
    - "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"
    - "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)