human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "distribution dependent and time uniform bounds for piecewise i i d bandits"):
  Abstract: We consider the setup of stochastic multi-armed bandits in the case when reward distributions are piecewise i.i.d. and bounded with unknown changepoints. We focus on the case when changes happen simultaneously on all arms, and in stark contrast with the existing literature, we target gap-dependent (as opposed to only gap-independent) regret bounds involving the magnitude of changes $(\Delta^{chg}_{i,g})$ and optimality-gaps ($\Delta^{opt}_{i,g}$). Diverging from previous works, we assume the more realistic scenario that there can be undetectable changepoint gaps and under a different set of assumptions, we show that as long as the compounded delayed detection for each changepoint is bounded there is no need for forced exploration to actively detect changepoints. We introduce two adaptations of UCB-strategies that employ scan-statistics in order to actively detect the changepoints, without knowing in advance the changepoints and also the mean before and after any change. Our first method \UCBLCPD does not know the number of changepoints $G$ or time horizon $T$ and achieves the first time-uniform concentration bound for this setting using the Laplace method of integration. The second strategy \ImpCPD makes use of the knowledge of $T$ to achieve the order optimal regret bound of $\min\big\lbrace O(\sum\limits_{i=1}^{K} \sum\limits_{g=1}^{G}\frac{\log(T/H_{1,g})}{\Delta^{opt}_{i,g}}), O(\sqrt{GT})\big\rbrace$, (where $H_{1,g}$ is the problem complexity) thereby closing an important gap with respect to the lower bound in a specific challenging setting. Our theoretical findings are supported by numerical experiments on synthetic and real-life datasets.

Includes members:
  Member "a change detection based framework for piecewise stationary multi armed bandit problem":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "combinatorial semi bandit in the non stationary environment")
  Member "tracking the best expert in non stationary stochastic environments":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "combinatorial semi bandit in the non stationary environment")
  Member "optimally confident ucb improved regret for finite armed bandits":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "accelerated learning from recommender systems using multi armed bandit")
  Member "thompson sampling in switching environments with bayesian online change point detection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "better boosting with bandits for online learning")

Question: Please tell me which class the source paper 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)