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: "deep kernels for optimizing locomotion controllers"
  Abstract: Sample efficiency is important when optimizing parameters of locomotion controllers, since hardware experiments are time consuming and expensive. Bayesian Optimization, a sample-efficient optimization framework, has recently been widely applied to address this problem, but further improvements in sample efficiency are needed for practical applicability to real-world robots and high-dimensional controllers. To address this, prior work has proposed using domain expertise for constructing custom distance metrics for locomotion. In this work we show how to learn such a distance metric automatically, without relying on domain experts. We use a neural network to learn an informed distance metric from data obtained in high-fidelity simulations. We conduct experiments on two different controllers and robot architectures. First, we demonstrate improvement in sample efficiency when optimizing a 5-dimensional controller on the ATRIAS robot hardware. We then conduct simulation experiments to optimize a 16-dimensional controller for a 7-link robot model and obtain significant improvements even when optimizing in perturbed environments. This demonstrates that our approach is able to enhance sample efficiency for two different controllers, hence is a fitting candidate for further experiments on hardware in the future.

Connected through hyperedge HE-1 (induced by source paper "botorch programmable bayesian optimization in pytorch"):
  with members:
    - "probo a framework for using probabilistic programming in bayesian optimization"
    - "probo versatile bayesian optimization using any probabilistic programming language"
    - "practical multi fidelity bayesian optimization for hyperparameter tuning"
    - "subspace inference for bayesian deep learning"
    - "a simple baseline for bayesian uncertainty in deep learning"
    - "pyro deep universal probabilistic programming"
    - "horizon facebook s open source applied reinforcement learning platform"
    - "gpytorch blackbox matrix matrix gaussian process inference with gpu acceleration"

Connected through hyperedge HE-2 (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 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"
    - "a sensorimotor reinforcement learning framework for physical human robot interaction"

Connected through hyperedge HE-3 (induced by source paper "interactive trajectory adaptation through force guided bayesian optimization"):
  with members:
    - "trajectory deformations from physical human robot interaction"
    - "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"
    - "automatic lqr tuning based on gaussian process global optimization"
    - "safe controller optimization for quadrotors with gaussian processes"
    - "robots that can adapt like animals"

Connected through hyperedge HE-4 (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"
    - "robust adversarial reinforcement learning"
    - "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-5 (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-6 (induced by source paper "sim to real learning agile locomotion for quadruped robots"):
  with members:
    - "deepmind control suite"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"
    - "robust adversarial reinforcement learning"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "preparing for the unknown learning a universal policy with online system identification"
    - "emergence of locomotion behaviours in rich environments"
    - "tensorflow agents efficient batched reinforcement learning in tensorflow"

Connected through hyperedge HE-7 (induced by source paper "global search with bernoulli alternation kernel for task oriented grasping informed by simulation"):
  with members:
    - "learning task oriented grasping for tool manipulation from simulated self supervision"
    - "using simulation to improve sample efficiency of bayesian optimization for bipedal robots"
    - "dex net 2 0 deep learning to plan robust grasps with synthetic point clouds and analytic grasp metrics"
    - "bayesian optimization using domain knowledge on the atrias biped"
    - "shape completion enabled robotic grasping"
    - "shapenet an information rich 3d model repository"
    - "robots that can adapt like animals"
    - "3d shapenets a deep representation for volumetric shapes"

Connected through hyperedge HE-8 (induced by source paper "using simulation to improve sample efficiency of bayesian optimization for bipedal robots"):
  with members:
    - "bayesian optimization using domain knowledge on the atrias biped"
    - "sample efficient optimization for learning controllers for bipedal locomotion"
    - "a tutorial on bayesian optimization of expensive cost functions with application to active user modeling and hierarchical reinforcement learning"

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)