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 humanoid locomotion using trajectory optimization and sample efficient learning"):
  Abstract: Trajectory optimization (TO) is one of the most powerful tools for generating feasible motions for humanoid robots. However, including uncertainties and stochasticity in the TO problem to generate robust motions can easily lead to intractable problems. Furthermore, since the models used in TO have always some level of abstraction, it can be hard to find a realistic set of uncertainties in the model space. In this paper we leverage a sample-efficient learning technique (Bayesian optimization) to robustify TO for humanoid locomotion. The main idea is to use data from full-body simulations to make the TO stage robust by tuning the cost weights. To this end, we split the TO problem into two phases. The first phase solves a convex optimization problem for generating center of mass (CoM) trajectories based on simplified linear dynamics. The second stage employs iterative Linear-Quadratic Gaussian (iLQG) as a whole-body controller to generate full body control inputs. Then we use Bayesian optimization to find the cost weights to use in the first stage that yields robust performance in the simulation/experiment, in the presence of different disturbance/uncertainties. The results show that the proposed approach is able to generate robust motions for different sets of disturbances and uncertainties.

Includes members:
  Member "locomotion planning through a hybrid bayesian trajectory optimization":
    also connected through other hyperedges:
      (none)
  Member "differential dynamic programming for multi phase rigid contact dynamics":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning how to walk warm starting optimal control solver with memory of motion")
  Member "walking control based on step timing adaptation":
    also connected through other hyperedges:
      (none)
  Member "pattern generation for walking on slippery terrains":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "trajectory optimization for robust humanoid locomotion with sample efficient learning")
  Member "bayesian optimization using domain knowledge on the atrias biped":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning fast adaptation with meta strategy optimization")
  Member "automatic lqr tuning based on gaussian process global optimization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "computing the racing line using bayesian optimization")
  Member "sample efficient optimization for learning controllers for bipedal locomotion":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "enhancement of energy based swing up controller via entropy search")
  Member "robots that can adapt like animals":
    also connected through other hyperedges:
      - Hyperedge HE-6 (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.RO(Robotics)