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 "learning piecewise lipschitz functions in changing environments"):
  Abstract: Optimization in the presence of sharp (non-Lipschitz), unpredictable (w.r.t.\ time and amount) changes is a challenging and largely unexplored problem of great significance. We consider the class of piecewise Lipschitz functions, which is the most general online 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 {\it 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 also present a lower bound tight up to sub-logarithmic factors. We further obtain improved bounds when selecting from a small pool of experts. We empirically demonstrate a key application of our algorithms to online clustering problems on popular benchmarks.

Includes members:
  Member "introduction to online convex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "beyond ucb optimal and efficient contextual bandits with regression oracles")
  Member "data driven clustering via parameterized lloyd s families":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "semi bandit optimization in the dispersed setting")
  Member "dispersion for data driven algorithm design online learning and private optimization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "empirical bayes regret minimization")
  Member "online optimization of smoothed piecewise constant functions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "semi bandit optimization in the dispersed setting")
  Member "learning theoretic foundations of algorithm configuration for combinatorial partitioning problems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning complexity of simulated annealing")
  Member "online optimization competing with dynamic comparators":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "minimizing dynamic regret and adaptive regret simultaneously")
  Member "strongly adaptive online learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "beyond ucb optimal and efficient contextual bandits with regression oracles")
  Member "an algorithm for online k means clustering":
    also connected through other hyperedges:
      - Hyperedge HE-8 (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)