human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "posterior variance analysis of gaussian processes with application to average learning curves"):
  Abstract: The posterior variance of Gaussian processes is a valuable measure of the learning error which is exploited in various applications such as safe reinforcement learning and control design. However, suitable analysis of the posterior variance which captures its behavior for finite and infinite number of training data is missing. This paper derives a novel bound for the posterior variance function which requires only local information because it depends only on the number of training samples in the proximity of a considered test point. Furthermore, we prove sufficient conditions which ensure the convergence of the posterior variance to zero. Finally, we demonstrate that the extension of our bound to an average learning bound outperforms existing approaches.

Includes members:
  Member "gaussian process based passivation of a class of nonlinear systems with unknown dynamics":
    also connected through other hyperedges:
      (none)
  Member "provably robust learning based approach for high accuracy tracking control of lagrangian systems":
    also connected through other hyperedges:
      (none)
  Member "learning based model predictive control for safe exploration":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "exploration exploitation in constrained mdps")
  Member "on kernelized multi armed bandits":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "mixed strategies for robust optimization of unknown objectives")
  Member "safe controller optimization for quadrotors with gaussian processes":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "beyond no regret competitive control via online optimization with memory")
  Member "bayesian optimization with safety constraints safe and automatic parameter tuning in robotics":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "probabilistic safety constraints for learned high relative degree system dynamics")
  Member "information theoretic regret bounds for gaussian process optimization in the bandit setting":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "efficient and robust algorithms for adversarial linear contextual bandits")

Question: Please tell me which class the source paper 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)