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: "follow the leader if you can hedge if you must"
  Abstract: Follow-the-Leader (FTL) is an intuitive sequential prediction strategy that guarantees constant regret in the stochastic setting, but has terrible performance for worst-case data. Other hedging strategies have better worst-case guarantees but may perform much worse than FTL if the data are not maximally adversarial. We introduce the FlipFlop algorithm, which is the first method that provably combines the best of both worlds. #R##N#As part of our construction, we develop AdaHedge, which is a new way of dynamically tuning the learning rate in Hedge without using the doubling trick. AdaHedge refines a method by Cesa-Bianchi, Mansour and Stoltz (2007), yielding slightly improved worst-case guarantees. By interleaving AdaHedge and FTL, the FlipFlop algorithm achieves regret within a constant factor of the FTL regret, without sacrificing AdaHedge's worst-case guarantees. #R##N#AdaHedge and FlipFlop do not need to know the range of the losses in advance; moreover, unlike earlier methods, both have the intuitive property that the issued weights are invariant under rescaling and translation of the losses. The losses are also allowed to be negative, in which case they may be interpreted as gains.

Connected through hyperedge HE-1 (induced by source paper "fast rates for online prediction with abstention"):
  with members:
    - "fast classification rates without standard margin assumptions"
    - "online learning with abstention"
    - "combining adversarial guarantees and stochastic fast rates in online learning"
    - "the extended littlestone s dimension for learning with mistakes and abstentions"
    - "fast rates in statistical and online learning"
    - "the sample complexity of learning linear predictors with the squared loss"

Connected through hyperedge HE-2 (induced by source paper "lipschitz adaptivity with multiple learning rates in online learning"):
  with members:
    - "introduction to online convex optimization"
    - "black box reductions for parameter free online learning in banach spaces"
    - "zigzag a new approach to adaptive online learning"
    - "online learning without prior information"
    - "metagrad multiple learning rates in online learning"
    - "combining adversarial guarantees and stochastic fast rates in online learning"
    - "second order quantile methods for experts and combinatorial games"
    - "achieving all with no parameters adaptive normalhedge"

Connected through hyperedge HE-3 (induced by source paper "adaptive hedging under delayed feedback"):
  with members:
    - "introduction to online convex optimization"
    - "aggregating strategies for long term forecasting"
    - "fast rates in statistical and online learning"
    - "second order quantile methods for experts and combinatorial games"
    - "prediction by random walk perturbation"
    - "online learning under delayed feedback"
    - "mirror descent meets fixed share and feels no regret"
    - "an identity for kernel ridge regression"

Connected through hyperedge HE-4 (induced by source paper "dying experts efficient algorithms with optimal regret bounds"):
  with members:
    - "hardness of online sleeping combinatorial optimization problems"

Connected through hyperedge HE-5 (induced by source paper "variational bayesian reinforcement learning with regret bounds"):
  with members:
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "a short variational proof of equivalence between policy gradients and soft q learning"
    - "equivalence between policy gradients and soft q learning"
    - "the uncertainty bellman equation and exploration"
    - "boltzmann exploration done right"
    - "a distributional perspective on reinforcement learning"
    - "count based exploration with neural density models"
    - "bridging the gap between value and policy based reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "more adaptive algorithms for adversarial bandits"):
  with members:
    - "introduction to online convex optimization"
    - "an improved parametrization and analysis of the exp3 algorithm for stochastic and adversarial bandits"
    - "small loss bounds for online learning with partial information"
    - "sparsity variance and curvature in multi armed bandits"
    - "corralling a band of bandit algorithms"
    - "metagrad multiple learning rates in online learning"
    - "kernel based methods for bandit convex optimization"
    - "coin betting and parameter free online learning"

Connected through hyperedge HE-7 (induced by source paper "lazy cfr fast and near optimal regret minimization for extensive games with imperfect information"):
  with members:
    - "solving imperfect information games via discounted regret minimization"
    - "deep counterfactual regret minimization"
    - "safe and nested subgame solving for imperfect information games"
    - "deepstack expert level artificial intelligence in heads up no limit poker"
    - "reduced space and faster convergence in imperfect information games via regret based pruning"

Connected through hyperedge HE-8 (induced by source paper "a simple parameter free and adaptive approach to optimization under a minimal local smoothness assumption"):
  with members:
    - "bandit algorithms for tree search"
    - "exponential regret bounds for gaussian process bandits with deterministic observations"
    - "the best of both worlds stochastic and adversarial bandits"
    - "multi armed bandits in metric spaces"

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)