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 "coin betting and parameter free online learning"):
  Abstract: In the recent years, a number of parameter-free algorithms have been developed for online linear optimization over Hilbert spaces and for learning with expert advice. These algorithms achieve optimal regret bounds that depend on the unknown competitors, without having to tune the learning rates with oracle choices. #R##N#We present a new intuitive framework to design parameter-free algorithms for \emph{both} online linear optimization over Hilbert spaces and for learning with expert advice, based on reductions to betting on outcomes of adversarial coins. We instantiate it using a betting algorithm based on the Krichevsky-Trofimov estimator. The resulting algorithms are simple, with no parameters to be tuned, and they improve or match previous results in terms of regret guarantee and per-round complexity.

Includes members:
  Member "adaptive online learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "adaptive online learning with varying norms")
  Member "second order quantile methods for experts and combinatorial games":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a modern introduction to online learning")
  Member "a drifting games analysis for online learning and applications to boosting":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "online influence maximization with local observations")
  Member "prediction with advice of unknown number of experts":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a modern introduction to online learning")
  Member "simultaneous model selection and optimization through parameter free stochastic learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "adaptive online learning with varying norms")
  Member "unconstrained online linear learning in hilbert spaces minimax algorithms and normal approximations":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "lipschitz and comparator norm adaptivity in online learning")
  Member "a pac bayesian tutorial with a dropout bound":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on the information bottleneck problems models connections applications and information theoretic views")
  Member "minimax optimal algorithms for unconstrained linear optimization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a modern introduction to online learning")

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)