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 "hedging the drift learning to optimize under non stationarity"):
  Abstract: We introduce general data-driven decision-making algorithms that achieve state-of-the-art dynamic regret bounds for non-stationary bandit settings. They applications such as advertisement allocation and dynamic pricing in changing environments. We show how the difficulty posed by the (unknown a priori and possibly adversarial) non-stationarity can be overcome by an unconventional marriage between stochastic and adversarial bandit learning algorithms. Our main contribution is a general algorithmic recipe that first converts the rate-optimal Upper-Confidence-Bound (UCB) algorithm for stationary bandit settings into a tuned Sliding Window-UCB algorithm with optimal dynamic regret for the corresponding non-stationary counterpart. Boosted by the novel bandit-over-bandit framework with automatic adaptation to the unknown changing environment, it even permits us to enjoy this optimal (or a nearly optimal) dynamic regret in a (surprisingly) parameter-free manner. . In addition to the classical exploration-exploitation trade-off, our algorithms leverage the power of the "forgetting principle" in their online learning processes, which is vital in changing environments. We further conduct extensive numerical experiments on both synthetic data and the CPRM-12-001: On-Line Auto Lending dataset provided by the Center for Pricing and Revenue Management at Columbia University to show that our proposed algorithms achieve superior dynamic regret performances.

Includes members:
  Member "a new algorithm for non stationary contextual bandits efficient optimal and parameter free":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "combinatorial semi bandit in the non stationary environment")
  Member "meta dynamic pricing learning across experiments":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "online pricing with offline data phase transition and inverse square law")
  Member "reinforcement learning under drift":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "provable self play algorithms for competitive reinforcement learning")
  Member "model selection for contextual bandits":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "beyond ucb optimal and efficient contextual bandits with regression oracles")
  Member "learning to optimize under non stationarity":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "efficient and robust algorithms for adversarial linear contextual bandits")
  Member "provably optimal algorithms for generalized linear contextual bandits":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "neural contextual bandits with ucb based exploration")
  Member "tracking the best expert in non stationary stochastic environments":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "combinatorial semi bandit in the non stationary environment")
  Member "corralling a band of bandit algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a closer look at small loss bounds for bandits with graph feedback")

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)