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: "a drifting games analysis for online learning and applications to boosting"
  Abstract: We provide a general mechanism to design online learning algorithms based on a minimax analysis within a drifting-games framework. Different online learning settings (Hedge, multi-armed bandit problems and online convex optimization) are studied by converting into various kinds of drifting games. The original minimax analysis for drifting games is then used and generalized by applying a series of relaxations, starting from choosing a convex surrogate of the 0-1 loss function. With different choices of surrogates, we not only recover existing algorithms, but also propose new algorithms that are totally parameter-free and enjoy other useful properties. Moreover, our drifting-games framework naturally allows us to study high probability bounds without resorting to any concentration results, and also a generalized notion of regret that measures how good the algorithm is compared to all but the top small fraction of candidates. Finally, we translate our new Hedge algorithm into a new adaptive boosting algorithm that is computationally faster as shown in experiments, since it ignores a large number of examples on each round.

Connected through hyperedge HE-1 (induced by source paper "online influence maximization with local observations"):
  with members:
    - "improving regret bounds for combinatorial semi bandits with probabilistically triggered arms and its applications"
    - "influence maximization with bandits"
    - "a parameter free hedging algorithm"

Connected through hyperedge HE-2 (induced by source paper "coin betting and parameter free online learning"):
  with members:
    - "adaptive online learning"
    - "second order quantile methods for experts and combinatorial games"
    - "prediction with advice of unknown number of experts"
    - "simultaneous model selection and optimization through parameter free stochastic learning"
    - "unconstrained online linear learning in hilbert spaces minimax algorithms and normal approximations"
    - "a pac bayesian tutorial with a dropout bound"
    - "minimax optimal algorithms for unconstrained linear optimization"
    - "no regret algorithms for unconstrained online convex optimization"

Connected through hyperedge HE-3 (induced by source paper "second order quantile methods for experts and combinatorial games"):
  with members:
    - "achieving all with no parameters adaptive normalhedge"
    - "prediction with advice of unknown number of experts"
    - "online linear optimization via smoothing"
    - "follow the leader if you can hedge if you must"
    - "regret in online combinatorial optimization"
    - "a parameter free hedging algorithm"
    - "adaptive online prediction by following the perturbed leader"

Connected through hyperedge HE-4 (induced by source paper "achieving all with no parameters adaptive normalhedge"):
  with members:
    - "prediction with advice of unknown number of experts"
    - "simultaneous model selection and optimization through parameter free stochastic learning"
    - "unconstrained online linear learning in hilbert spaces minimax algorithms and normal approximations"
    - "follow the leader if you can hedge if you must"
    - "no regret algorithms for unconstrained online convex optimization"
    - "a parameter free hedging algorithm"

Connected through hyperedge HE-5 (induced by source paper "optimal and adaptive algorithms for online boosting"):
  with members:
    - "an online boosting algorithm with theoretical justifications"

Connected through hyperedge HE-6 (induced by source paper "simultaneous model selection and optimization through parameter free stochastic learning"):
  with members:
    - "unconstrained online linear learning in hilbert spaces minimax algorithms and normal approximations"
    - "non strongly convex smooth stochastic approximation with convergence rate o 1 n"
    - "no regret algorithms for unconstrained online convex optimization"
    - "less regret via online conditioning"
    - "a parameter free hedging algorithm"

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)