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: "adaptive online learning"
  Abstract: We propose a general framework for studying adaptive regret bounds in the online learning framework, including model selection bounds and data-dependent bounds. Given a data- or model-dependent bound we ask, "Does there exist some algorithm achieving this bound?" We show that modifications to recently introduced sequential complexity measures can be used to answer this question by providing sufficient conditions under which adaptive rates can be achieved. In particular each adaptive rate induces a set of so-called offset complexity measures, and obtaining small upper bounds on these quantities is sufficient to demonstrate achievability. A cornerstone of our analysis technique is the use of one-sided tail inequalities to bound suprema of offset random processes. #R##N#Our framework recovers and improves a wide variety of adaptive bounds including quantile bounds, second-order data-dependent bounds, and small loss bounds. In addition we derive a new type of adaptive bound for online linear optimization based on the spectral norm, as well as a new online PAC-Bayes theorem that holds for countably infinite sets.

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 "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"
    - "coin betting and parameter free online learning"
    - "bolt on differential privacy for scalable stochastic gradient descent based analytics"
    - "second order quantile methods for experts and combinatorial games"

Connected through hyperedge HE-3 (induced by source paper "coin betting and parameter free online learning"):
  with members:
    - "second order quantile methods for experts and combinatorial games"
    - "a drifting games analysis for online learning and applications to boosting"
    - "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-4 (induced by source paper "bistro an efficient relaxation based method for contextual bandits"):
  with members:
    - "efficient algorithms for adversarial contextual learning"
    - "hierarchies of relaxations for online prediction problems with evolving constraints"
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "efficient transductive online learning via randomized rounding"
    - "efficient optimal learning for contextual bandits"
    - "contextual bandit algorithms with supervised learning guarantees"

Connected through hyperedge HE-5 (induced by source paper "online learning in repeated auctions"):
  with members:
    - "dynamic reserve prices for repeated auctions learning from bids"
    - "the sample complexity of revenue maximization"
    - "optimal auctions for correlated buyers with sampling"
    - "online learning and profit maximization from revealed preferences"
    - "mechanism design for data science"
    - "learning valuation distributions from partial observation"
    - "bandits with knapsacks"
    - "learning theory and algorithms for revenue optimization in second price auctions with reserve"

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)