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: "online optimization of smoothed piecewise constant functions"
  Abstract: We study online optimization of smoothed piecewise constant functions over the domain [0, 1). This is motivated by the problem of adaptively picking parameters of learning algorithms as in the recently introduced framework by Gupta and Roughgarden (2016). Majority of the machine learning literature has focused on Lipschitz-continuous functions or functions with bounded gradients. This is with good reason—any learning algorithm suffers linear regret even against piecewise constant functions that are chosen adversarially, arguably the simplest of non-Lipschitz continuous functions. The smoothed setting we consider is inspired by the seminal work of Spielman and Teng (2004) and the recent work of Gupta and Roughgarden (2016)—in this setting, the sequence of functions may be chosen by an adversary, however, with some uncertainty in the location of discontinuities. We give algorithms that achieve sublinear regret in the full information and bandit settings.

Connected through hyperedge HE-1 (induced by source paper "learning piecewise lipschitz functions in changing environments"):
  with members:
    - "introduction to online convex optimization"
    - "data driven clustering via parameterized lloyd s families"
    - "dispersion for data driven algorithm design online learning and private optimization"
    - "learning theoretic foundations of algorithm configuration for combinatorial partitioning problems"
    - "online optimization competing with dynamic comparators"
    - "strongly adaptive online learning"
    - "an algorithm for online k means clustering"
    - "mirror descent meets fixed share and feels no regret"

Connected through hyperedge HE-2 (induced by source paper "semi bandit optimization in the dispersed setting"):
  with members:
    - "data driven clustering via parameterized lloyd s families"
    - "leapsandbounds a method for approximately optimal algorithm configuration"
    - "dispersion for data driven algorithm design online learning and private optimization"
    - "a general theory of sample complexity for multi item profit maximization"
    - "learning theoretic foundations of algorithm configuration for combinatorial partitioning problems"
    - "k center clustering under perturbation resilience"
    - "a pac approach to application specific algorithm selection"
    - "online learning with feedback graphs beyond bandits"

Connected through hyperedge HE-3 (induced by source paper "learning to optimize computational resources frugal training with generalization guarantees"):
  with members:
    - "semi bandit optimization in the dispersed setting"
    - "learning to prune speeding up repeated computations"
    - "procrastinating with confidence near optimal anytime adaptive algorithm configuration"
    - "learning to link"
    - "learning to branch"
    - "learning robust search strategies using a bandit based approach"
    - "leapsandbounds a method for approximately optimal algorithm configuration"
    - "dispersion for data driven algorithm design online learning and private optimization"

Connected through hyperedge HE-4 (induced by source paper "learning to prune speeding up repeated computations"):
  with members:
    - "learning to branch"
    - "leapsandbounds a method for approximately optimal algorithm configuration"
    - "dispersion for data driven algorithm design online learning and private optimization"
    - "osmnx new methods for acquiring constructing analyzing and visualizing complex street networks"
    - "learning theoretic foundations of algorithm configuration for combinatorial partitioning problems"
    - "a pac approach to application specific algorithm selection"
    - "self improving algorithms"

Connected through hyperedge HE-5 (induced by source paper "online optimization of piecewise lipschitz functions in changing environments"):
  with members:
    - "semi bandit optimization in the dispersed setting"
    - "introduction to online convex optimization"
    - "dispersion for data driven algorithm design online learning and private optimization"
    - "learning theoretic foundations of algorithm configuration for combinatorial partitioning problems"
    - "an algorithm for online k means clustering"

Connected through hyperedge HE-6 (induced by source paper "dispersion for data driven algorithm design online learning and private optimization"):
  with members:
    - "learning to bid without knowing your value"
    - "a general theory of sample complexity for multi item profit maximization"
    - "revenue optimization with approximate bid predictions"
    - "a sample complexity measure with applications to learning optimal auctions"
    - "efficient empirical revenue maximization in single parameter auction environments"
    - "learning theoretic foundations of algorithm configuration for combinatorial partitioning problems"
    - "the sample complexity of revenue maximization"
    - "a pac approach to application specific algorithm selection"

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)