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 for budgeted multi armed bandits"
  Abstract: Thompson sampling is one of the earliest randomized algorithms for multi-armed bandits (MAB). In this paper, we extend the Thompson sampling to Budgeted MAB, where there is random cost for pulling an arm and the total cost is constrained by a budget. We start with the case of Bernoulli bandits, in which the random rewards (costs) of an arm are independently sampled from a Bernoulli distribution. To implement the Thompson sampling algorithm in this case, at each round, we sample two numbers from the posterior distributions of the reward and cost for each arm, obtain their ratio, select the arm with the maximum ratio, and then update the posterior distributions. We prove that the distribution-dependent regret bound of this algorithm is $O(\ln B)$, where $B$ denotes the budget. By introducing a Bernoulli trial, we further extend this algorithm to the setting that the rewards (costs) are drawn from general distributions, and prove that its regret bound remains almost the same. Our simulation results demonstrate the effectiveness of the proposed algorithm.

Connected through hyperedge HE-1 (induced by source paper "online joint bid daily budget optimization of internet advertising campaigns"):
  with members:
    - "bid optimization by multivariable control in display advertising"
    - "budget constrained bidding by model free reinforcement learning in display advertising"
    - "combinatorial semi bandits with knapsacks"
    - "display advertising with real time bidding rtb and behavioural targeting"
    - "feedback control of real time display advertising"
    - "combinatorial semi bandit with known covariance"
    - "multi touch attribution based budget allocation in online advertising"
    - "smart pacing for effective online ad campaign optimization"

Connected through hyperedge HE-2 (induced by source paper "budget constrained bandits over general cost and reward distributions"):
  with members:
    - "combinatorial semi bandits with knapsacks"
    - "linear contextual bandits with knapsacks"
    - "bandits with concave rewards and convex knapsacks"
    - "resourceful contextual bandits"
    - "bandits with knapsacks"
    - "knapsack based optimal policies for budget limited multi armed bandits"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"

Connected through hyperedge HE-3 (induced by source paper "achieving fairness in the stochastic multi armed bandit problem"):
  with members:
    - "advancing subgroup fairness via sleeping experts"
    - "fair logistic regression an adversarial perspective"
    - "fairness in recommendation ranking through pairwise comparisons"
    - "combinatorial sleeping bandits with fairness constraints"
    - "policy learning for fairness in ranking"
    - "equal opportunity in online classification with partial feedback"
    - "introduction to multi armed bandits"
    - "online learning with an unknown fairness metric"

Connected through hyperedge HE-4 (induced by source paper "simple algorithms for dueling bandits"):
  with members:
    - "a tutorial on thompson sampling"
    - "multi dueling bandits with dependent arms"
    - "double thompson sampling for dueling bandits"
    - "copeland dueling bandits"
    - "contextual dueling bandits"
    - "an information theoretic analysis of thompson sampling"
    - "learning to optimize via posterior sampling"
    - "relative upper confidence bound for the k armed dueling bandit problem"

Connected through hyperedge HE-5 (induced by source paper "multi armed bandit for energy efficient and delay sensitive edge computing in dynamic networks with uncertainty"):
  with members:
    - "computation offloading and activation of mobile edge computing servers a minority game"
    - "multi armed bandits with application to 5g small cells"
    - "optimization of radio and computational resources for energy efficiency in latency constrained application offloading"
    - "knapsack based optimal policies for budget limited multi armed bandits"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"

Connected through hyperedge HE-6 (induced by source paper "multi armed bandits with compensation"):
  with members:
    - "bayesian exploration incentivizing exploration in bayesian games"
    - "knapsack based optimal policies for budget limited multi armed bandits"
    - "further optimal regret bounds for thompson sampling"
    - "contextual bandit algorithms with supervised learning guarantees"

Connected through hyperedge HE-7 (induced by source paper "double thompson sampling for dueling bandits"):
  with members:
    - "algorithms with logarithmic or sublinear regret for constrained contextual bandits"
    - "an information theoretic analysis of thompson sampling"
    - "reducing dueling bandits to cardinal bandits"
    - "learning to optimize via posterior sampling"
    - "relative upper confidence bound for the k armed dueling bandit problem"
    - "further optimal regret bounds for thompson sampling"

Connected through hyperedge HE-8 (induced by source paper "unimodal thompson sampling for graph structured arms"):
  with members:
    - "unimodal bandits regret lower bounds and optimal algorithms"
    - "unimodal bandits without smoothness"
    - "from bandits to experts a tale of domination and independence"
    - "from bandits to experts on the value of side observations"
    - "multi armed bandits in metric spaces"

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)