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: "combining adversarial guarantees and stochastic fast rates in online learning"
  Abstract: We consider online learning algorithms that guarantee worst-case regret rates in adversarial environments (so they can be deployed safely and will perform robustly), yet adapt optimally to favorable stochastic environments (so they will perform well in a variety of settings of practical importance). We quantify the friendliness of stochastic environments by means of the well-known Bernstein (a.k.a. generalized Tsybakov margin) condition. For two recent algorithms (Squint for the Hedge setting and MetaGrad for online convex optimization) we show that the particular form of their data-dependent individual-sequence regret guarantees implies that they adapt automatically to the Bernstein parameters of the stochastic environment. We prove that these algorithms attain fast rates in their respective settings both in expectation and with high probability.

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"
    - "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"
    - "follow the leader if you can hedge if you must"

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"
    - "second order quantile methods for experts and combinatorial games"
    - "achieving all with no parameters adaptive normalhedge"
    - "prediction with advice of unknown number of experts"

Connected through hyperedge HE-3 (induced by source paper "pac bayes un expected bernstein inequality"):
  with members:
    - "computing nonvacuous generalization bounds for deep stochastic neural networks with many more parameters than training data"
    - "fast rates with high probability in exp concave statistical learning"
    - "fast rates for general unbounded loss functions from erm to generalized bayes"
    - "fast rates in statistical and online learning"
    - "from stochastic mixability to fast rates"
    - "a note on the pac bayesian theorem"

Connected through hyperedge HE-4 (induced by source paper "beating stochastic and adversarial semi bandits optimally and simultaneously"):
  with members:
    - "more adaptive algorithms for adversarial bandits"
    - "an improved parametrization and analysis of the exp3 algorithm for stochastic and adversarial bandits"
    - "sparsity variance and curvature in multi armed bandits"
    - "combinatorial bandits revisited"
    - "first order regret bounds for combinatorial semi bandits"
    - "the best of both worlds stochastic and adversarial bandits"

Connected through hyperedge HE-5 (induced by source paper "metagrad multiple learning rates in online learning"):
  with members:
    - "efficient second order online learning by sketching"
    - "coin betting and parameter free online learning"
    - "deep learning in neural networks"
    - "fast rates in statistical and online learning"
    - "second order quantile methods for experts and combinatorial games"
    - "efficient estimation of word representations in vector space"
    - "a generalized online mirror descent with applications to classification and regression"
    - "adadelta an adaptive learning rate method"

Connected through hyperedge HE-6 (induced by source paper "fast rates for general unbounded loss functions from erm to generalized bayes"):
  with members:
    - "a tight excess risk bound via a unified pac bayesian rademacher shtarkov mdl complexity"
    - "fast rates in statistical and online learning"
    - "learning without concentration"
    - "from stochastic mixability to fast rates"
    - "renyi divergence and kullback leibler divergence"
    - "minimax analysis of active learning"
    - "follow the leader if you can hedge if you must"
    - "relative deviation learning bounds and generalization with unbounded loss functions"

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)