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: "regret of queueing bandits"
  Abstract: We consider a variant of the multiarmed bandit problem where jobs queue for service, and service rates of different servers may be unknown. We study algorithms that minimize queueregret: the (expected) difference between the queue-lengths obtained by the algorithm, and those obtained by a “genie”-aided matching algorithm that knows exact service rates. A naive view of this problem would suggest that queue-regret should grow logarithmically: since queue-regret cannot be larger than classical regret, results for the standard MAB problem give algorithms that ensure queue-regret increases no more than logarithmically in time. Our paper shows surprisingly more complex behavior. In particular, the naive intuition is correct as long as the bandit algorithm’s queues have relatively long regenerative cycles: in this case queue-regret is similar to cumulative regret, and scales (essentially) logarithmically. However, we show that this “early stage” of the queueing bandit eventually gives way to a “late stage”, where the optimal queue-regret scaling is O(1/t). We demonstrate an algorithm that (order-wise) achieves this asymptotic queue-regret, and also exhibits close to optimal switching time from the early stage to the late stage.

Connected through hyperedge HE-1 (induced by source paper "stability and learning in strategic queuing systems"):
  with members:
    - "pacing equilibrium in first price auction markets"
    - "multiplicative pacing equilibria in auction markets"
    - "small loss bounds for online learning with partial information"
    - "the fitness level method with tail bounds"
    - "composable and efficient mechanisms"

Connected through hyperedge HE-2 (induced by source paper "optimal learning for dynamic coding in deadline constrained multi channel networks"):
  with members:
    - "risk sensitive reinforcement learning for urllc traffic in wireless networks"
    - "bounded regret for finite armed structured bandits"
    - "millimeter wave cellular wireless networks potentials and challenges"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"

Connected through hyperedge HE-3 (induced by source paper "belman bayesian bandits on the belief reward manifold"):
  with members:
    - "simple bayesian algorithms for best arm identification"
    - "an information theoretic analysis of thompson sampling"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"

Connected through hyperedge HE-4 (induced by source paper "learning unknown service rates in queues a multi armed bandit approach"):
  with members:
    - "combinatorial semi bandit with known covariance"
    - "combinatorial bandits revisited"
    - "congestion control of tcp flows in internet routers by means of index policy"
    - "regret bounds for restless markov bandits"
    - "analysis of thompson sampling for the multi armed bandit problem"

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.SY(Systems and Control)