human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "sample efficient optimization for learning controllers for bipedal locomotion"
  Abstract: Learning policies for bipedal locomotion can be difficult, as experiments are expensive and simulation does not usually transfer well to hardware. To counter this, we need algorithms that are sample efficient and inherently safe. Bayesian Optimization is a powerful sample-efficient tool for optimizing non-convex black-box functions. However, its performance can degrade in higher dimensions. We develop a distance metric for bipedal locomotion that enhances the sample-efficiency of Bayesian Optimization and use it to train a 16 dimensional neuromuscular model for planar walking. This distance metric reflects some basic gait features of healthy walking and helps us quickly eliminate a majority of unstable controllers. With our approach we can learn policies for walking in less than 100 trials for a range of challenging settings. In simulation, we show results on two different costs and on various terrains including rough ground and ramps, sloping upwards and downwards. We also perturb our models with unknown inertial disturbances analogous with differences between simulation and hardware. These results are promising, as they indicate that this method can potentially be used to learn control policies on hardware.

Connected through hyperedge HE-1 (induced by source paper "robust humanoid locomotion using trajectory optimization and sample efficient learning"):
  with members:
    - "locomotion planning through a hybrid bayesian trajectory optimization"
    - "differential dynamic programming for multi phase rigid contact dynamics"
    - "walking control based on step timing adaptation"
    - "pattern generation for walking on slippery terrains"
    - "bayesian optimization using domain knowledge on the atrias biped"
    - "automatic lqr tuning based on gaussian process global optimization"
    - "robots that can adapt like animals"
    - "trajectory generation for multi contact momentum control"

Connected through hyperedge HE-2 (induced by source paper "enhancement of energy based swing up controller via entropy search"):
  with members:
    - "virtual vs real trading off simulations and physical experiments in reinforcement learning with bayesian optimization"
    - "automatic lqr tuning based on gaussian process global optimization"
    - "safe controller optimization for quadrotors with gaussian processes"

Connected through hyperedge HE-3 (induced by source paper "a survey on policy search algorithms for learning robot controllers in a handful of trials"):
  with members:
    - "learning agile and dynamic motor skills for legged robots"
    - "reset free trial and error learning for robot damage recovery"
    - "an algorithmic perspective on imitation learning"
    - "composable deep reinforcement learning for robotic manipulation"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "world models"
    - "sim to real learning agile locomotion for quadruped robots"
    - "state representation learning for control an overview"

Connected through hyperedge HE-4 (induced by source paper "beyond basins of attraction evaluating robustness of natural dynamics"):
  with members:
    - "walking with confidence safety regulation for full order biped models"
    - "gait learning for soft microrobots controlled by light fields"
    - "learning from outside the viability kernel why we should build robots that can fall with grace"
    - "real time control for autonomous racing based on viability theory"
    - "robust optimal planning and control of non periodic bipedal locomotion with a centroidal momentum model"
    - "bayesian optimization using domain knowledge on the atrias biped"
    - "shaping in practice training wheels to learn fast hopping directly in hardware"
    - "hamilton jacobi reachability a brief overview and recent advances"

Connected through hyperedge HE-5 (induced by source paper "efficient model identification for tensegrity locomotion"):
  with members:
    - "preparing for the unknown learning a universal policy with online system identification"
    - "virtual vs real trading off simulations and physical experiments in reinforcement learning with bayesian optimization"
    - "deep reinforcement learning for tensegrity robot locomotion"
    - "a convex polynomial force motion model for planar sliding identification and application"

Connected through hyperedge HE-6 (induced by source paper "using simulation to improve sample efficiency of bayesian optimization for bipedal robots"):
  with members:
    - "deep kernels for optimizing locomotion controllers"
    - "bayesian optimization using domain knowledge on the atrias biped"
    - "a tutorial on bayesian optimization of expensive cost functions with application to active user modeling and hierarchical reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "using parameterized black box priors to scale up model based policy search for robotics"):
  with members:
    - "reset free trial and error learning for robot damage recovery"
    - "gp ilqg data driven robust optimal control for uncertain nonlinear dynamical systems"
    - "limbo a fast and flexible library for bayesian optimization"
    - "funnel libraries for real time robust feedback motion planning"
    - "continuous control with deep reinforcement learning"
    - "trust region policy optimization"
    - "model based reinforcement learning with parametrized physical models and optimism driven exploration"
    - "illuminating search spaces by mapping elites"

Connected through hyperedge HE-8 (induced by source paper "deep kernels for optimizing locomotion controllers"):
  with members:
    - "virtual vs real trading off simulations and physical experiments in reinforcement learning with bayesian optimization"
    - "momentum control with hierarchical inverse dynamics on a torque controlled humanoid"
    - "robots that can adapt like animals"

Question: Please tell me which class the center node 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)