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: "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.

Connected through hyperedge HE-1 (induced by source paper "adaptive online learning with varying norms"):
  with members:
    - "extreme tensoring for low memory preconditioning"
    - "adaptive scale invariant online algorithms for learning linear models"
    - "parameter free online convex optimization with sub exponential noise"
    - "matrix free preconditioning in online learning"
    - "shampoo preconditioned stochastic tensor optimization"
    - "black box reductions for parameter free online learning in banach spaces"
    - "online learning sufficient statistics and the burkholder method"
    - "scale invariant unconstrained online learning"

Connected through hyperedge HE-2 (induced by source paper "lipschitz and comparator norm adaptivity in online learning"):
  with members:
    - "adaptive scale invariant online algorithms for learning linear models"
    - "introduction to online convex optimization"
    - "lipschitz adaptivity with multiple learning rates in online learning"
    - "black box reductions for parameter free online learning in banach spaces"
    - "online learning sufficient statistics and the burkholder method"
    - "scale invariant unconstrained online learning"
    - "zigzag a new approach to adaptive online learning"
    - "online learning without prior information"

Connected through hyperedge HE-3 (induced by source paper "locally adaptive nonparametric online learning"):
  with members:
    - "introduction to online convex optimization"
    - "gated linear networks"
    - "lipschitz adaptivity with multiple learning rates in online learning"
    - "nonparametric online regression while learning the metric"
    - "metagrad multiple learning rates in online learning"
    - "online learning via sequential complexities"
    - "metric entropy in competitive on line prediction"
    - "competing with wild prediction rules"

Connected through hyperedge HE-4 (induced by source paper "online learning with imperfect hints"):
  with members:
    - "adaptive scale invariant online algorithms for learning linear models"
    - "near optimal bounds for online caching with machine learned advice"
    - "parameter free online convex optimization with sub exponential noise"
    - "matrix free preconditioning in online learning"
    - "semi online bipartite matching"
    - "black box reductions for parameter free online learning in banach spaces"
    - "competitive caching with machine learned advice"
    - "online learning sufficient statistics and the burkholder method"

Connected through hyperedge HE-5 (induced by source paper "a modern introduction to online learning"):
  with members:
    - "on the convergence of adam and beyond"
    - "black box reductions for parameter free online learning in banach spaces"
    - "a modular analysis of adaptive non convex optimization optimism composite objectives and variational bounds"
    - "training deep networks without learning rates through coin betting"
    - "scale free online learning"
    - "fighting bandits with a new kind of smoothness"
    - "second order quantile methods for experts and combinatorial games"
    - "prediction with advice of unknown number of experts"

Connected through hyperedge HE-6 (induced by source paper "matrix free preconditioning in online learning"):
  with members:
    - "adaptive scale invariant online algorithms for learning linear models"
    - "shampoo preconditioned stochastic tensor optimization"
    - "black box reductions for parameter free online learning in banach spaces"
    - "the case for full matrix adaptive regularization"
    - "attention is all you need"
    - "training deep networks without learning rates through coin betting"
    - "online learning without prior information"
    - "efficient second order online learning by sketching"

Connected through hyperedge HE-7 (induced by source paper "adaptive scale invariant online algorithms for learning linear models"):
  with members:
    - "introduction to online convex optimization"
    - "black box reductions for parameter free online learning in banach spaces"
    - "training deep networks without learning rates through coin betting"
    - "online learning without prior information"
    - "scale free algorithms for online linear optimization"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"
    - "simultaneous model selection and optimization through parameter free stochastic learning"

Connected through hyperedge HE-8 (induced by source paper "parameter free online convex optimization with sub exponential noise"):
  with members:
    - "black box reductions for parameter free online learning in banach spaces"
    - "online learning sufficient statistics and the burkholder method"
    - "scale invariant unconstrained online learning"
    - "training deep networks without learning rates through coin betting"
    - "online learning without prior information"
    - "bolt on differential privacy for scalable stochastic gradient descent based analytics"
    - "adaptive online learning"
    - "second order quantile methods for experts and combinatorial games"

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)