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: "automatic lqr tuning based on gaussian process global optimization"
  Abstract: This paper proposes an automatic controller tuning framework based on linear optimal control combined with Bayesian optimization. With this framework, an initial set of controller gains is automatically improved according to a pre-defined performance objective evaluated from experimental data. The underlying Bayesian optimization algorithm is Entropy Search, which represents the latent objective as a Gaussian process and constructs an explicit belief over the location of the objective minimum. This is used to maximize the information gain from each experimental evaluation. Thus, this framework shall yield improved controllers with fewer evaluations compared to alternative approaches. A seven-degree-of-freedom robot arm balancing an inverted pole is used as the experimental demonstrator. Results of two- and four-dimensional tuning problems highlight the method's potential for automatic controller tuning on robotic platforms.

Connected through hyperedge HE-1 (induced by source paper "computing the racing line using bayesian optimization"):
  with members:
    - "botorch programmable bayesian optimization in pytorch"
    - "learning how to autonomously race a car a predictive control approach"
    - "path tracking of highly dynamic autonomous vehicle trajectories via iterative learning control"
    - "a contextual bandit approach to personalized news article recommendation"

Connected through hyperedge HE-2 (induced by source paper "a survey of behavior learning applications in robotics state of the art and perspectives"):
  with members:
    - "learning dexterous in hand manipulation"
    - "deepmimic example guided deep reinforcement learning of physics based character skills"
    - "deep haptic model predictive control for robot assisted dressing"
    - "one shot visual imitation learning via meta learning"
    - "deep reinforcement learning a brief survey"
    - "learning complex dexterous manipulation with deep reinforcement learning and demonstrations"
    - "combining model based and model free updates for trajectory centric reinforcement learning"
    - "time contrastive networks self supervised learning from video"

Connected through hyperedge HE-3 (induced by source paper "bayesian optimization meets riemannian manifolds in robot learning"):
  with members:
    - "riemannian motion policies"
    - "bayesian optimization with automatic prior selection for data efficient direct policy search"
    - "deep kernels for optimizing locomotion controllers"
    - "deep reinforcement learning a brief survey"
    - "bayesian optimization using domain knowledge on the atrias biped"
    - "on the design of lqr kernels for efficient controller learning"
    - "pymanopt a python toolbox for optimization on manifolds using automatic differentiation"
    - "learning dynamic robot to human object handover from human feedback"

Connected through hyperedge HE-4 (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"
    - "sample efficient optimization for learning controllers for bipedal locomotion"
    - "robots that can adapt like animals"
    - "trajectory generation for multi contact momentum control"

Connected through hyperedge HE-5 (induced by source paper "interactive trajectory adaptation through force guided bayesian optimization"):
  with members:
    - "trajectory deformations from physical human robot interaction"
    - "deep kernels for optimizing locomotion controllers"
    - "mathematical models of adaptation in human robot collaboration"
    - "learning dynamic robot to human object handover from human feedback"
    - "a sensorimotor reinforcement learning framework for physical human robot interaction"
    - "safe controller optimization for quadrotors with gaussian processes"
    - "robots that can adapt like animals"

Connected through hyperedge HE-6 (induced by source paper "classified regression for bayesian optimization robot learning with unknown penalties"):
  with members:
    - "gait learning for soft microrobots controlled by light fields"
    - "a survey on policy search algorithms for learning robot controllers in a handful of trials"
    - "least squares temporal difference learning for the linear quadratic regulator"
    - "bayesian optimization using domain knowledge on the atrias biped"
    - "safe controller optimization for quadrotors with gaussian processes"
    - "expectation propagation for approximate bayesian inference"

Connected through hyperedge HE-7 (induced by source paper "robust model free reinforcement learning with multi objective bayesian optimization"):
  with members:
    - "data efficient auto tuning with bayesian optimization an industrial control study"
    - "recovering robustness in model free reinforcement learning"
    - "deep kernels for optimizing locomotion controllers"
    - "robust adversarial reinforcement learning"
    - "virtual vs real trading off simulations and physical experiments in reinforcement learning with bayesian optimization"
    - "safe controller optimization for quadrotors with gaussian processes"

Connected through hyperedge HE-8 (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"
    - "sample efficient optimization for learning controllers for bipedal locomotion"
    - "safe controller optimization for quadrotors with gaussian processes"

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)