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 "uniform error bounds for gaussian process regression with application to safe control"):
  Abstract: Data-driven models are subject to model errors due to limited and noisy training data. Key to the application of such models in safety-critical domains is the quantification of their model error. Gaussian processes provide such a measure and uniform error bounds have been derived, which allow safe control based on these models. However, existing error bounds require restrictive assumptions. In this paper, we employ the Gaussian process distribution and continuity arguments to derive a novel uniform error bound under weaker assumptions. Furthermore, we demonstrate how this distribution can be used to derive probabilistic Lipschitz constants and analyze the asymptotic behavior of our bound. Finally, we derive safety conditions for the control of unknown dynamical systems based on Gaussian process models and evaluate them in simulations of a robotic manipulator.

Includes members:
  Member "posterior variance analysis of gaussian processes with application to average learning curves":
    also connected through other hyperedges:
      (none)
  Member "stable gaussian process based tracking control of euler lagrange systems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "episodic learning with control lyapunov functions for uncertain robotic systems")
  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 learning of regions of attraction for uncertain nonlinear systems with gaussian processes":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "episodic learning with control lyapunov functions for uncertain robotic systems")
  Member "an empirical evaluation of deep learning on highway driving":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "semi local 3d lane detection and uncertainty estimation")
  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)