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: "online learning under delayed feedback"
  Abstract: Online learning with delayed feedback has received increasing attention recently due to its several applications in distributed, web-based learning problems. In this paper we provide a systematic study of the topic, and analyze the effect of delay on the regret of online learning algorithms. Somewhat surprisingly, it turns out that delay increases the regret in a multiplicative way in adversarial problems, and in an additive way in stochastic problems. We give meta-algorithms that transform, in a black-box fashion, algorithms developed for the non-delayed case into ones that can handle the presence of delays in the feedback loop. Modifications of the well-known UCB algorithm are also developed for the bandit problem with delayed feedback, with the advantage over the meta-algorithms that they can be implemented with lower complexity.

Connected through hyperedge HE-1 (induced by source paper "logarithmic regret for adversarial online control"):
  with members:
    - "logarithmic regret for learning linear quadratic regulators efficiently"
    - "improper learning for non stochastic control"
    - "naive exploration is optimal for online lqr"
    - "online convex optimization in adversarial markov decision processes"
    - "introduction to online convex optimization"
    - "online control with adversarial disturbances"
    - "logarithmic regret for online control"
    - "the nonstochastic control problem"

Connected through hyperedge HE-2 (induced by source paper "delay adaptive learning in generalized linear contextual bandits"):
  with members:
    - "provably optimal algorithms for generalized linear contextual bandits"
    - "thompson sampling for the mnl bandit"
    - "delay and cooperation in nonstochastic bandits"
    - "asymptotic convergence in online learning with unbounded delays"
    - "off policy evaluation for slate recommendation"
    - "an efficient algorithm for contextual bandits with knapsacks and an extension to concave objectives"
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "an information theoretic analysis of thompson sampling"

Connected through hyperedge HE-3 (induced by source paper "a feedback shift correction in predicting conversion rates under delayed feedback"):
  with members:
    - "reacting to variations in product demand an application for conversion rate cr prediction in sponsored search"
    - "a nonparametric delayed feedback model for conversion rate prediction"
    - "detecting and correcting for label shift with black box predictors"
    - "field aware factorization machines in a real world online advertising system"
    - "stochastic bandit models for delayed conversions"

Connected through hyperedge HE-4 (induced by source paper "a farewell to arms sequential reward maximization on a budget with a giving up option"):
  with members:
    - "hyperband a novel bandit based approach to hyperparameter optimization"
    - "logarithmic regret bounds for bandits with knapsacks"
    - "online learning in repeated auctions"
    - "bandits with knapsacks"
    - "knapsack based optimal policies for budget limited multi armed bandits"

Connected through hyperedge HE-5 (induced by source paper "an online learning framework for energy efficient navigation of electric vehicles"):
  with members:
    - "thompson sampling for combinatorial semi bandits"
    - "efficient online learning for optimizing value of information theory and application to interactive troubleshooting"

Connected through hyperedge HE-6 (induced by source paper "decentralized cooperative stochastic bandits"):
  with members:
    - "distributed cooperative decision making in multiarmed bandits frequentist and bayesian algorithms"
    - "distributed clustering of linear bandits in peer to peer networks"
    - "delay and cooperation in nonstochastic bandits"
    - "on distributed cooperative decision making in multiarmed bandits"
    - "distributed exploration in multi armed bandits"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "distributed algorithms for learning and cognitive medium access with logarithmic regret"
    - "gossip algorithms for distributed signal processing"

Connected through hyperedge HE-7 (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"
    - "follow the leader if you can hedge if you must"
    - "prediction by random walk perturbation"
    - "mirror descent meets fixed share and feels no regret"
    - "an identity for kernel ridge regression"

Connected through hyperedge HE-8 (induced by source paper "parallel contextual bandits in wireless handover optimization"):
  with members:
    - "stochastic bandit models for delayed conversions"
    - "low rank bandits with latent mixtures"
    - "parallel predictive entropy search for batch global optimization of expensive objective functions"
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "parallel gaussian process optimization with upper confidence bound and pure exploration"
    - "further optimal regret bounds for thompson sampling"
    - "thompson sampling for contextual bandits with linear payoffs"
    - "multiarmed bandit problems with delayed feedback"

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)