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: "gp ilqg data driven robust optimal control for uncertain nonlinear dynamical systems"
  Abstract: As we aim to control complex systems, use of a simulator in model-based reinforcement learning is becoming more common. However, it has been challenging to overcome the Reality Gap, which comes from nonlinear model bias and susceptibility to disturbance. To address these problems, we propose a novel algorithm that combines data-driven system identification approach (Gaussian Process) with a Differential-Dynamic-Programming-based robust optimal control method (Iterative Linear Quadratic Control). Our algorithm uses the simulator's model as the mean function for a Gaussian Process and learns only the difference between the simulator's prediction and actual observations, making it a natural hybrid of simulation and real-world observation. We show that our approach quickly corrects incorrect models, comes up with robust optimal controllers, and transfers its acquired model knowledge to new tasks efficiently.

Connected through hyperedge HE-1 (induced by source paper "sufficiently accurate model learning"):
  with members:
    - "learning optimal resource allocations in wireless systems"
    - "deepmind control suite"
    - "visual reinforcement learning with imagined goals"
    - "se3 pose nets structured deep dynamics models for visuomotor planning and control"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"
    - "constrained policy optimization"
    - "memory augmented control networks"
    - "model agnostic meta learning for fast adaptation of deep networks"

Connected through hyperedge HE-2 (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-3 (induced by source paper "safe reinforcement learning with model uncertainty estimates"):
  with members:
    - "motion planning among dynamic decision making agents with deep reinforcement learning"
    - "spatial uncertainty sampling for end to end control"
    - "fully distributed multi robot collision avoidance via deep reinforcement learning for safe and efficient navigation in complex scenarios"
    - "stein variational policy gradient"
    - "social attention modeling attention in human crowds"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "socially aware motion planning with deep reinforcement learning"
    - "predictive positioning and quality of service ridesharing for campus mobility on demand systems"

Connected through hyperedge HE-4 (induced by source paper "reinforcement learning for non prehensile manipulation transfer from simulation to physical system"):
  with members:
    - "sim to real transfer of robotic control with dynamics randomization"
    - "learning complex dexterous manipulation with deep reinforcement learning and demonstrations"
    - "collective robot reinforcement learning with distributed asynchronous guided policy search"
    - "towards generalization and simplicity in continuous control"
    - "proximal policy optimization algorithms"
    - "epopt learning robust neural network policies using model ensembles"
    - "deep reinforcement learning for robotic manipulation with asynchronous off policy updates"
    - "path integral guided policy search"

Connected through hyperedge HE-5 (induced by source paper "synthesizing neural network controllers with probabilistic model based reinforcement learning"):
  with members:
    - "continuous control with deep reinforcement learning"
    - "learning continuous control policies by stochastic value gradients"
    - "adam a method for stochastic optimization"
    - "pegasus a policy search method for large mdps and pomdps"
    - "on the difficulty of training recurrent neural networks"

Connected through hyperedge HE-6 (induced by source paper "semiparametrical gaussian processes learning of forward dynamical models for navigating in a circular maze"):
  with members:
    - "augmenting physical simulators with stochastic neural networks case study of planar pushing and bouncing"
    - "combining learned and analytical models for predicting action effects"
    - "data efficient reinforcement learning with probabilistic model predictive control"
    - "end to end training of deep visuomotor policies"

Connected through hyperedge HE-7 (induced by source paper "bayesian optimization with automatic prior selection for data efficient direct policy search"):
  with members:
    - "reset free trial and error learning for robot damage recovery"
    - "deep kernels for optimizing locomotion controllers"
    - "deep reinforcement learning a brief survey"
    - "soft tensegrity robots"
    - "limbo a fast and flexible library for bayesian optimization"
    - "safety aware robot damage recovery using constrained bayesian optimization and simulated priors"
    - "safe controller optimization for quadrotors with gaussian processes"
    - "micro data learning the other end of the spectrum"

Connected through hyperedge HE-8 (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"
    - "limbo a fast and flexible library for bayesian optimization"
    - "funnel libraries for real time robust feedback motion planning"
    - "sample efficient optimization for learning controllers for bipedal locomotion"
    - "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"

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)