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 "robust policy search for robot navigation with stochastic meta policies"):
  Abstract: Bayesian optimization is an efficient nonlinear optimization method where the queries are carefully selected to gather information about the optimum location. Thus, in the context of policy search, it has been called active policy search. The main ingredients of Bayesian optimization for sample efficiency are the probabilistic surrogate model and the optimal decision heuristics. In this work, we exploit those to provide robustness to different issues for policy search algorithms. We combine several methods and show how their interaction works better than the sum of the parts. First, to deal with input noise and provide a safe and repeatable policy we use an improved version of unscented Bayesian optimization. Then, to deal with mismodeling errors and improve exploration we use stochastic meta-policies for query selection and an adaptive kernel. We compare the proposed algorithm with previous results in several optimization benchmarks and robot tasks, such as pushing objects with a robot arm, or path finding with a rover.

Includes members:
  Member "fully distributed bayesian optimization with stochastic policies":
    also connected through other hyperedges:
      (none)
  Member "bayesian optimisation under uncertain inputs":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "mixed strategies for robust optimization of unknown objectives")
  Member "using simulation to improve sample efficiency of bayesian optimization for bipedal robots":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "truly proximal policy optimization")
  Member "practical bayesian optimization in the presence of outliers":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "mixed strategies for robust optimization of unknown objectives")
  Member "unscented bayesian optimization for safe robot grasping":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "mixed strategies for robust optimization of unknown objectives")
  Member "funneled bayesian optimization for design tuning and control of autonomous systems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "fully distributed bayesian optimization with stochastic policies")
  Member "parallel predictive entropy search for batch global optimization of expensive objective functions":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "one shot bayes opt with probabilistic population based training")
  Member "robots that can adapt like animals":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "explore discover and learn unsupervised discovery of state covering skills")

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)