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: "thompson sampling in switching environments with bayesian online change point detection"
  Abstract: Thompson Sampling has recently been shown to be optimal in the Bernoulli Multi-Armed Bandit setting[Kaufmann et al., 2012]. This bandit problem assumes stationary distributions for the rewards. It is often unrealistic to model the real world as a stationary distribution. In this paper we derive and evaluate algorithms using Thompson Sampling for a Switching Multi-Armed Bandit Problem. We propose a Thompson Sampling strategy equipped with a Bayesian change point mechanism to tackle this problem. We develop algorithms for a variety of cases with constant switching rate: when switching occurs all arms change (Global Switching), switching occurs independently for each arm (Per-Arm Switching), when the switching rate is known and when it must be inferred from data. This leads to a family of algorithms we collectively term Change-Point Thompson Sampling (CTS). We show empirical results of the algorithm in 4 artificial environments, and 2 derived from real world data; news click-through[Yahoo!, 2011] and foreign exchange data[Dukascopy, 2012], comparing them to some other bandit algorithms. In real world data CTS is the most effective.

Connected through hyperedge HE-1 (induced by source paper "better boosting with bandits for online learning"):
  with members:
    - "online isotonic regression"
    - "further optimal regret bounds for thompson sampling"
    - "obtaining calibrated probabilities from boosting"
    - "unbiased offline evaluation of contextual bandit based news article recommendation algorithms"
    - "analysis of thompson sampling for the multi armed bandit problem"
    - "multi armed bandits in metric spaces"

Connected through hyperedge HE-2 (induced by source paper "a near optimal change detection based algorithm for piecewise stationary combinatorial semi bandits"):
  with members:
    - "combinatorial semi bandit in the non stationary environment"
    - "beating stochastic and adversarial semi bandits optimally and simultaneously"
    - "introduction to online convex optimization"
    - "bandit online learning with unknown delays"
    - "thompson sampling for combinatorial semi bandits"
    - "a change detection based framework for piecewise stationary multi armed bandit problem"
    - "sparsity variance and curvature in multi armed bandits"
    - "collaborative filtering bandits"

Connected through hyperedge HE-3 (induced by source paper "be aware of non stationarity nearly optimal algorithms for piecewise stationary cascading bandits"):
  with members:
    - "a near optimal change detection based algorithm for piecewise stationary combinatorial semi bandits"
    - "cascading non stationary bandits online learning to rank in the non stationary cascade model"
    - "bandit online learning with unknown delays"
    - "thompson sampling algorithms for cascading bandits"
    - "a change detection based framework for piecewise stationary multi armed bandit problem"
    - "online learning to rank in stochastic click models"
    - "combinatorial bandits revisited"
    - "cascading bandits learning to rank in the cascade model"

Connected through hyperedge HE-4 (induced by source paper "distribution dependent and time uniform bounds for piecewise i i d bandits"):
  with members:
    - "a change detection based framework for piecewise stationary multi armed bandit problem"
    - "tracking the best expert in non stationary stochastic environments"
    - "optimally confident ucb improved regret for finite armed bandits"

Connected through hyperedge HE-5 (induced by source paper "a change detection based framework for piecewise stationary multi armed bandit problem"):
  with members:
    - "tracking the best expert in non stationary stochastic environments"
    - "reward maximization under uncertainty leveraging side observations on networks"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "unbiased offline evaluation of contextual bandit based news article recommendation algorithms"
    - "analysis of thompson sampling for the multi armed bandit problem"

Connected through hyperedge HE-6 (induced by source paper "multi armed bandit problem with known trend"):
  with members:
    - "a neural networks committee for the contextual bandit problem"

Connected through hyperedge HE-7 (induced by source paper "generalized thompson sampling for sequential decision making and causal inference"):
  with members:
    - "learning to optimize via posterior sampling"
    - "monte carlo utility estimates for bayesian reinforcement learning"
    - "a bayesian sampling approach to exploration in reinforcement learning"
    - "information utility bounded rationality"
    - "analysis of thompson sampling for the multi armed bandit problem"
    - "a bayesian rule for adaptive control based on causal interventions"
    - "a minimum relative entropy principle for learning and acting"
    - "time space and energy in reversible computing"

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)