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: "stochastic bandit models for delayed conversions"
  Abstract: Online advertising and product recommendation are important domains of applications for multi-armed bandit methods. In these fields, the reward that is immediately available is most often only a proxy for the actual outcome of interest, which we refer to as a conversion. For instance, in web advertising, clicks can be observed within a few seconds after an ad display but the corresponding sale --if any-- will take hours, if not days to happen. This paper proposes and investigates a new stochas-tic multi-armed bandit model in the framework proposed by Chapelle (2014) --based on empirical studies in the field of web advertising-- in which each action may trigger a future reward that will then happen with a stochas-tic delay. We assume that the probability of conversion associated with each action is unknown while the distribution of the conversion delay is known, distinguishing between the (idealized) case where the conversion events may be observed whatever their delay and the more realistic setting in which late conversions are censored. We provide performance lower bounds as well as two simple but efficient algorithms based on the UCB and KLUCB frameworks. The latter algorithm, which is preferable when conversion rates are low, is based on a Poissonization argument, of independent interest in other settings where aggregation of Bernoulli observations with different success probabilities is required.

Connected through hyperedge HE-1 (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"
    - "online learning under delayed feedback"

Connected through hyperedge HE-2 (induced by source paper "nonstochastic multiarmed bandits with unrestricted delays"):
  with members:
    - "stochastic bandits with delayed composite anonymous feedback"
    - "an optimal algorithm for adversarial bandits with arbitrary delays"
    - "bandit online learning with unknown delays"
    - "online scoring with delayed information a convex optimization viewpoint"
    - "learning from delayed outcomes with intermediate observations"
    - "online learning with local permutations and delayed feedback"
    - "delay and cooperation in nonstochastic bandits"
    - "asymptotic convergence in online learning with unbounded delays"

Connected through hyperedge HE-3 (induced by source paper "parallel contextual bandits in wireless handover optimization"):
  with members:
    - "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"
    - "online learning under delayed feedback"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "stochastic bandits with delayed composite anonymous feedback"):
  with members:
    - "delay and cooperation in nonstochastic bandits"
    - "asymptotic convergence in online learning with unbounded delays"
    - "online learning with composite loss functions"
    - "online learning under delayed feedback"
    - "efficient optimal learning for contextual bandits"

Connected through hyperedge HE-5 (induced by source paper "bandit online learning with unknown delays"):
  with members:
    - "introduction to online convex optimization"
    - "adaptation to easy data in prediction with limited advice"
    - "online learning with local permutations and delayed feedback"
    - "delay and cooperation in nonstochastic bandits"
    - "communication efficient learning of deep networks from decentralized data"
    - "online learning under delayed feedback"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "a contextual bandit approach to personalized news article recommendation"

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)