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: "unimodal bandits regret lower bounds and optimal algorithms"
  Abstract: We consider stochastic multi-armed bandits where the expected reward is a unimodal function over partially ordered arms. This important class of problems has been recently investigated in (Cope 2009, Yu 2011). The set of arms is either discrete, in which case arms correspond to the vertices of a finite graph whose structure represents similarity in rewards, or continuous, in which case arms belong to a bounded interval. For discrete unimodal bandits, we derive asymptotic lower bounds for the regret achieved under any algorithm, and propose OSUB, an algorithm whose regret matches this lower bound. Our algorithm optimally exploits the unimodal structure of the problem, and surprisingly, its asymptotic regret does not depend on the number of arms. We also provide a regret upper bound for OSUB in non-stationary environments where the expected rewards smoothly evolve over time. The analytical results are supported by numerical experiments showing that OSUB performs significantly better than the state-of-the-art algorithms. For continuous sets of arms, we provide a brief discussion. We show that combining an appropriate discretization of the set of arms with the UCB algorithm yields an order-optimal regret, and in practice, outperforms recently proposed algorithms designed to exploit the unimodal structure.

Connected through hyperedge HE-1 (induced by source paper "closed form whittle s index enabled random access for timely status update"):
  with members:
    - "age of information in g g 1 1 systems age expressions bounds special cases and optimization"
    - "age of information scaling in large networks with hierarchical cooperation"
    - "using erasure feedback for online timely updating with an energy harvesting sensor"
    - "can decentralized status update achieve universally near optimal age of information in wireless multiaccess channels"
    - "information aging through queues a mutual information perspective"
    - "delay performance of multi antenna multicasting in wireless networks"
    - "towards massive connectivity support for scalable mmtc communications in 5g networks"
    - "age optimal sampling and transmission scheduling in multi source systems"

Connected through hyperedge HE-2 (induced by source paper "multi armed bandits on partially revealed unit interval graphs"):
  with members:
    - "adaptive shortest path routing under unknown and stochastically varying link states"
    - "further optimal regret bounds for thompson sampling"
    - "leveraging side observations in stochastic bandits"
    - "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-3 (induced by source paper "can decentralized status update achieve universally near optimal age of information in wireless multiaccess channels"):
  with members:
    - "scheduling policies for minimizing age of information in broadcast wireless networks"
    - "timely status update in massive iot systems decentralized scheduling for wireless uplinks"
    - "status updates through m g 1 1 queues with harq"
    - "update or wait how to keep your data fresh"
    - "on the age of information in status update systems with packet management"

Connected through hyperedge HE-4 (induced by source paper "exploration in structured reinforcement learning"):
  with members:
    - "posterior sampling for reinforcement learning worst case regret bounds"
    - "online regret bounds for undiscounted continuous reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "regional multi armed bandits"):
  with members:
    - "from bandits to experts on the value of side observations"

Connected through hyperedge HE-6 (induced by source paper "efficient beam alignment in millimeter wave systems using contextual bandits"):
  with members:
    - "agile millimeter wave networks with provable guarantees"
    - "millimeter wave beam selection using out of band spatial information"
    - "energy efficient power and bandwidth allocation in an integrated sub 6 ghz millimeter wave system"
    - "coordinated initial access in millimetre wave standalone networks"
    - "unimodal thompson sampling for graph structured arms"
    - "directional cell discovery in millimeter wave cellular networks"
    - "dynamic rate and channel selection in cognitive radio systems"
    - "optimal rate sampling in 802 11 systems"

Connected through hyperedge HE-7 (induced by source paper "unimodal thompson sampling for graph structured arms"):
  with members:
    - "thompson sampling for budgeted multi armed bandits"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "sequential ranking under random semi bandit feedback"):
  with members:
    - "combinatorial cascading bandits"
    - "combinatorial bandits revisited"
    - "cascading bandits learning to rank in the cascade model"
    - "stochastic and adversarial combinatorial bandits"

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)