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: "optimal discovery with probabilistic expert advice finite time analysis and macroscopic optimality"
  Abstract: We consider an original problem that arises from the issue of security analysis of a power system and that we name optimal discovery with probabilistic expert advice. We address it with an algorithm based on the optimistic paradigm and on the Good-Turing missing mass estimator. We prove two different regret bounds on the performance of this algorithm under weak assumptions on the probabilistic experts. Under more restrictive hypotheses, we also prove a macroscopic optimality result, comparing the algorithm both with an oracle strategy and with uniform sampling. Finally, we provide numerical experiments illustrating these theoretical findings.

Connected through hyperedge HE-1 (induced by source paper "algorithms for online influencer marketing"):
  with members:
    - "robust influence maximization"
    - "online influence maximization under independent cascade model with semi bandit feedback"
    - "robust influence maximization"
    - "distinguishing between topical and non topical information diffusion mechanisms in social media"
    - "stop and stare optimal sampling algorithms for viral marketing in billion scale networks"
    - "online influence maximization"
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "combinatorial multi armed bandit and its extension to probabilistically triggered arms"

Connected through hyperedge HE-2 (induced by source paper "learning the distribution with largest mean two bandit frameworks"):
  with members:
    - "towards instance optimal bounds for best arm identification"
    - "learning to detect an oddball target"
    - "unimodal bandits without smoothness"
    - "further optimal regret bounds for thompson sampling"
    - "analysis of thompson sampling for the multi armed bandit problem"

Connected through hyperedge HE-3 (induced by source paper "the online discovery problem and its application to lifelong reinforcement learning"):
  with members:
    - "efficient model learning from joint action demonstrations for human robot collaborative tasks"
    - "pac bounds for discounted mdps"
    - "powerplay training an increasingly general problem solver by continually searching for the simplest still unsolvable problem"

Connected through hyperedge HE-4 (induced by source paper "the online coupon collector problem and its application to lifelong reinforcement learning"):
  with members:
    - "efficient model learning from joint action demonstrations for human robot collaborative tasks"
    - "pac bounds for discounted mdps"
    - "powerplay training an increasingly general problem solver by continually searching for the simplest still unsolvable 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.LG(Machine Learning)