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 "online optimization of piecewise lipschitz functions in changing environments"):
  Abstract: In an online optimization problem we are required to choose a sequence of points from a fixed feasible subset of $\mathbb{R}^d$. After each point is chosen, a function over the set is revealed and we accumulate payoff equal to the function value at the point. We consider the class of piecewise Lipschitz functions, which is the most general setting considered in the literature for the problem, and arises naturally in various combinatorial algorithm selection problems where utility functions can have sharp discontinuities. The usual performance metric of `static' regret minimizes the gap between the payoff accumulated and that of the best fixed point for the entire duration, and thus fails to capture changing environments. Shifting regret is a useful alternative, which allows for up to $s$ environment shifts. In this work we provide an $O(\sqrt{sdT\log T}+sT^{1-\beta})$ regret bound for $\beta$-dispersed functions, where $\beta$ roughly quantifies the rate at which discontinuities appear in the utility functions in expectation (typically $\beta\ge1/2$ in problems of practical interest). We show this bound is optimal up to sub-logarithmic factors. We further show how to improve the bounds when selecting from a small pool of experts. We empirically demonstrate a key application of our algorithms to online clustering problems, with 15-40% relative gain over static regret based algorithms on popular benchmarks.

Includes members:
  Member "semi bandit optimization in the dispersed setting":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning to optimize computational resources frugal training with generalization guarantees")
  Member "introduction to online convex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "beyond ucb optimal and efficient contextual bandits with regression oracles")
  Member "dispersion for data driven algorithm design online learning and private optimization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning piecewise lipschitz functions in changing environments")
  Member "online optimization of smoothed piecewise constant functions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning piecewise lipschitz functions in changing environments")
  Member "learning theoretic foundations of algorithm configuration for combinatorial partitioning problems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning piecewise lipschitz functions in changing environments")
  Member "an algorithm for online k means clustering":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "explainable k means and k medians clustering")

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)