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: "bandit problems with side observations"
  Abstract: An extension of the traditional two-armed bandit problem is considered, in which the decision maker has access to some side information before deciding which arm to pull. At each time t, before making a selection, the decision maker is able to observe a random variable X/sub t/ that provides some information on the rewards to be obtained. The focus is on finding uniformly good rules (that minimize the growth rate of the inferior sampling time) and on quantifying how much the additional information helps. Various settings are considered and for each setting, lower bounds on the achievable inferior sampling time are developed and asymptotically optimal adaptive schemes achieving these lower bounds are constructed.

Connected through hyperedge HE-1 (induced by source paper "bounded regret for finitely parameterized multi armed bandits"):
  with members:
    - "cascading bandits for large scale recommendation problems"
    - "bounded regret for finite armed structured bandits"
    - "thompson sampling for contextual bandits with linear payoffs"
    - "leveraging side observations in stochastic bandits"
    - "analysis of thompson sampling for the multi armed bandit problem"

Connected through hyperedge HE-2 (induced by source paper "contextual bandits with random projection"):
  with members:
    - "linear bandits in high dimension and recommendation systems"
    - "low rank approximation and regression in input sparsity time"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "a contextual bandit approach to personalized news article recommendation"

Connected through hyperedge HE-3 (induced by source paper "exploring offline policy evaluation for the continuous armed bandit problem"):
  with members:
    - "lock in feedback in sequential experiments"
    - "algorithms for multi armed bandit problems"
    - "analysis of thompson sampling for the multi armed bandit problem"

Connected through hyperedge HE-4 (induced by source paper "target tracking for contextual bandits application to demand side management"):
  with members:
    - "finite time analysis of kernelised contextual bandits"
    - "contextual bandit algorithms with supervised learning guarantees"

Connected through hyperedge HE-5 (induced by source paper "garbage in reward out bootstrapping exploration in multi armed bandits"):
  with members:
    - "perturbed history exploration in stochastic multi armed bandits"
    - "perturbed history exploration in stochastic linear bandits"
    - "new insights into bootstrapping for bandits"
    - "provably optimal algorithms for generalized linear contextual bandits"
    - "a practical method for solving contextual bandit problems using decision trees"
    - "online stochastic linear optimization under one bit feedback"
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "thompson sampling with the online bootstrap"

Connected through hyperedge HE-6 (induced by source paper "adaptive sequential experiments with unknown information flows"):
  with members:
    - "meta dynamic pricing learning across experiments"
    - "bandit learning with positive externalities"
    - "estimating the causal impact of recommendation systems from observational data"
    - "optimal exploration exploitation in a multi armed bandit problem with non stationary rewards"
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "real time bidding benchmarking with ipinyou dataset"
    - "empirical analysis of predictive algorithms for collaborative filtering"
    - "real time bid optimization with smooth budget delivery in online advertising"

Connected through hyperedge HE-7 (induced by source paper "new insights into bootstrapping for bandits"):
  with members:
    - "provably optimal algorithms for generalized linear contextual bandits"
    - "a practical method for solving contextual bandit problems using decision trees"
    - "deep exploration via bootstrapped dqn"
    - "online stochastic linear optimization under one bit feedback"
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "thompson sampling with the online bootstrap"
    - "api design for machine learning software experiences from the scikit learn project"
    - "further optimal regret bounds for thompson sampling"

Connected through hyperedge HE-8 (induced by source paper "the k nearest neighbour ucb algorithm for multi armed bandits with covariates"):
  with members:
    - "minimax rates for cost sensitive learning on manifolds with approximate nearest neighbours"
    - "learning with a wasserstein loss"
    - "new analysis of manifold embeddings and signal recovery from compressive measurements"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "contextual bandit algorithms with supervised learning guarantees"
    - "contextual bandits with similarity information"

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.IT(Information Theory)