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 "combining simulations and real robot experiments for bayesian optimization of bipedal gait stabilization"):
  Abstract: Walking controllers often require parametrization which must be tuned according to some cost function. To estimate these parameters, simulations can be performed which are cheap but do not fully represent reality. Real-robot experiments, on the other hand, are more expensive and lead to hardware wear-off. In this paper, we propose an approach for combining simulations and real experiments to learn gait stabilization parameters. We use a Bayesian optimization method which selects the most informative points in parameter space to evaluate based on the entropy of the cost function to optimize. Experiments with the igus Humanoid Open Platform demonstrate the effectiveness of our approach.

Includes members:
  Member "fused angles a representation of body orientation for balance":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "nimbro robots winning robocup 2018 humanoid adultsize soccer competitions")
  Member "omnidirectional bipedal walking with direct fused angle feedback mechanisms":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "nimbro robots winning robocup 2018 humanoid adultsize soccer competitions")
  Member "child sized 3d printed igus humanoid open platform":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "online visual robot tracking and identification using deep lstm networks")
  Member "bayesian optimization using domain knowledge on the atrias biped":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning fast adaptation with meta strategy optimization")
  Member "virtual vs real trading off simulations and physical experiments in reinforcement learning with bayesian optimization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "cyborg an autonomous cyber operations research gym")
  Member "automatic lqr tuning based on gaussian process global optimization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "computing the racing line using bayesian optimization")
  Member "safe controller optimization for quadrotors with gaussian processes":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "beyond no regret competitive control via online optimization with memory")

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.RO(Robotics)