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: "unscented bayesian optimization for safe robot grasping"
  Abstract: We address the robot grasp optimization problem of unknown objects considering uncertainty in the input space. Grasping unknown objects can be achieved by using a trial and error exploration strategy. Bayesian optimization is a sample efficient optimization algorithm that is especially suitable for this setups as it actively reduces the number of trials for learning about the function to optimize. In fact, this active object exploration is the same strategy that infants do to learn optimal grasps. One problem that arises while learning grasping policies is that some configurations of grasp parameters may be very sensitive to error in the relative pose between the object and robot end-effector. We call these configurations unsafe because small errors during grasp execution may turn good grasps into bad grasps. Therefore, to reduce the risk of grasp failure, grasps should be planned in safe areas. We propose a new algorithm, Unscented Bayesian optimization that is able to perform sample efficient optimization while taking into consideration input noise to find safe optima. The contribution of Unscented Bayesian optimization is twofold as if provides a new decision process that drives exploration to safe regions and a new selection procedure that chooses the optimal in terms of its safety without extra analysis or computational cost. Both contributions are rooted on the strong theory behind the unscented transformation, a popular nonlinear approximation method. We show its advantages with respect to the classical Bayesian optimization both in synthetic problems and in realistic robot grasp simulations. The results highlights that our method achieves optimal and robust grasping policies after few trials while the selected grasps remain in safe regions.

Connected through hyperedge HE-1 (induced by source paper "mixed strategies for robust optimization of unknown objectives"):
  with members:
    - "cooperation aware reinforcement learning for merging in dense traffic"
    - "bayesian optimisation under uncertain inputs"
    - "no regret learning in unknown games with correlated payoffs"
    - "safe intersection management for mixed transportation systems with human driven and autonomous vehicles"
    - "distributionally robust submodular maximization"
    - "hierarchical game theoretic planning for autonomous vehicles"
    - "practical bayesian optimization in the presence of outliers"
    - "robust optimization for non convex objectives"

Connected through hyperedge HE-2 (induced by source paper "robust policy search for robot navigation with stochastic meta policies"):
  with members:
    - "fully distributed bayesian optimization with stochastic policies"
    - "bayesian optimisation under uncertain inputs"
    - "using simulation to improve sample efficiency of bayesian optimization for bipedal robots"
    - "practical bayesian optimization in the presence of outliers"
    - "funneled bayesian optimization for design tuning and control of autonomous systems"
    - "parallel predictive entropy search for batch global optimization of expensive objective functions"
    - "robots that can adapt like animals"
    - "heteroscedastic treed bayesian optimisation"

Connected through hyperedge HE-3 (induced by source paper "bayesian optimisation for safe navigation under localisation uncertainty"):
  with members:
    - "information theoretic regret bounds for gaussian process optimization in the bandit setting"
    - "a tutorial on bayesian optimization of expensive cost functions with application to active user modeling and hierarchical reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "bayesian optimisation under uncertain inputs"):
  with members:
    - "bayesian optimisation for safe navigation under localisation uncertainty"
    - "bayesian optimization with expensive integrands"
    - "on kernelized multi armed bandits"
    - "a tutorial on bayesian optimization of expensive cost functions with application to active user modeling and hierarchical reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "developmental bayesian optimization of black box with visual similarity based transfer learning"):
  with members:
    - "learning flexible and reusable locomotion primitives for a microrobot"
    - "comparing task simplifications to learn closed loop object picking using deep reinforcement learning"
    - "virtual vs real trading off simulations and physical experiments in reinforcement learning with bayesian optimization"
    - "symbol emergence in robotics a survey"
    - "robots that can adapt like animals"
    - "3d shapenets a deep representation for volumetric shapes"
    - "a tutorial on bayesian optimization of expensive cost functions with application to active user modeling and hierarchical reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "doubly bayesian optimization"):
  with members:
    - "planning chemical syntheses with deep neural networks and symbolic ai"
    - "convolutional networks on graphs for learning molecular fingerprints"
    - "a compilation target for probabilistic programming languages"
    - "venture a higher order probabilistic programming platform with programmable inference"
    - "church a language for generative models"

Connected through hyperedge HE-7 (induced by source paper "practical bayesian optimization in the presence of outliers"):
  with members:
    - "bayesopt a bayesian optimization library for nonlinear optimization experimental design and bandits"

Connected through hyperedge HE-8 (induced by source paper "active preference learning for personalized portfolio construction"):
  with members:
    - "evolutionary multimodal optimization a short survey"

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)