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: "reinforcement learning for non prehensile manipulation transfer from simulation to physical system"
  Abstract: Reinforcement learning has emerged as a promising methodology for training robot controllers. However, most results have been limited to simulation due to the need for a large number of samples and the lack of automated-yet-safe data collection methods. Model-based reinforcement learning methods provide an avenue to circumvent these challenges, but the traditional concern has been the mismatch between the simulator and the real world. Here, we show that control policies learned in simulation can successfully transfer to a physical system, composed of three Phantom robots pushing an object to various desired target positions. We use a modified form of the natural policy gradient algorithm for learning, applied to a carefully identified simulation model. The resulting policies, trained entirely in simulation, work well on the physical system without additional training. In addition, we show that training with an ensemble of models makes the learned policies more robust to modeling errors, thus compensating for difficulties in system identification.

Connected through hyperedge HE-1 (induced by source paper "bayesian domain randomization for sim to real transfer"):
  with members:
    - "self paced contextual reinforcement learning"
    - "active domain randomization"
    - "assessing transferability from simulation to reality for reinforcement learning"
    - "botorch programmable bayesian optimization in pytorch"
    - "learning dexterous in hand manipulation"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "learning to simulate"
    - "fingerprint policy optimisation for robust reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "benchmarking in hand manipulation"):
  with members:
    - "learning dexterous in hand manipulation"
    - "dexterous manipulation graphs"
    - "pneumatic shape shifting fingers to reorient and grasp"
    - "relaxed rigidity constraints kinematic trajectory optimization and collision avoidance for in grasp manipulation"
    - "in hand manipulation via motion cones"
    - "geometric in hand regrasp planning alternating optimization of finger gaits and in grasp manipulation"
    - "reinforcement learning for pivoting task"
    - "stable prehensile pushing in hand manipulation with alternating sticking contacts"

Connected through hyperedge HE-3 (induced by source paper "learning fast adaptation with meta strategy optimization"):
  with members:
    - "data efficient learning for sim to real robotic grasping using deep point cloud prediction networks"
    - "efficient off policy meta reinforcement learning via probabilistic context variables"
    - "sim to real transfer for biped locomotion"
    - "norml no reward meta learning"
    - "learning agile and dynamic motor skills for legged robots"
    - "simple random search provides a competitive approach to reinforcement learning"
    - "learning dexterous in hand manipulation"
    - "policy transfer with strategy optimization"

Connected through hyperedge HE-4 (induced by source paper "generalization through simulation integrating simulated and real data into deep reinforcement learning for vision based autonomous flight"):
  with members:
    - "vr goggles for robots real to sim domain adaptation for visual control"
    - "deep drone racing learning agile flight in dynamic environments"
    - "deep reinforcement learning for vision based robotic grasping a simulated comparative evaluation of off policy methods"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "gibson env real world perception for embodied agents"
    - "experiments in fast autonomous gps denied quadrotor flight"
    - "ultra low power deep learning powered autonomous nano drones"
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"

Connected through hyperedge HE-5 (induced by source paper "multi vehicle mixed reality reinforcement learning for autonomous multi lane driving"):
  with members:
    - "a fleet of miniature cars for experiments in cooperative driving"
    - "learning safe unlabeled multi robot planning with motion constraints"
    - "sim to multi real transfer of low level robust control policies to multiple quadrotors"
    - "deepracer educational autonomous racing platform for experimentation with sim2real reinforcement learning"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "sim to real learning agile locomotion for quadruped robots"
    - "learning to adapt in dynamic real world environments through meta reinforcement learning"
    - "closing the sim to real loop adapting simulation randomization with real world experience"

Connected through hyperedge HE-6 (induced by source paper "sim to real transfer for biped locomotion"):
  with members:
    - "learning agile and dynamic motor skills for legged robots"
    - "learning symmetric and low energy locomotion"
    - "learning dexterous in hand manipulation"
    - "policy transfer with strategy optimization"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "deepmimic example guided deep reinforcement learning of physics based character skills"
    - "sim to real learning agile locomotion for quadruped robots"
    - "hardware conditioned policies for multi robot transfer learning"

Connected through hyperedge HE-7 (induced by source paper "modelling generalized forces with reinforcement learning for sim to real transfer"):
  with members:
    - "learning agile and dynamic motor skills for legged robots"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "sim to real learning agile locomotion for quadruped robots"
    - "reinforcement and imitation learning for diverse visuomotor skills"
    - "augmenting physical simulators with stochastic neural networks case study of planar pushing and bouncing"
    - "model based reinforcement learning via meta policy optimization"
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"

Connected through hyperedge HE-8 (induced by source paper "sim to multi real transfer of low level robust control policies to multiple quadrotors"):
  with members:
    - "neuroflight next generation flight control firmware"
    - "generalization through simulation integrating simulated and real data into deep reinforcement learning for vision based autonomous flight"
    - "sim to real learning agile locomotion for quadruped robots"
    - "synthetically trained neural networks for learning human readable plans from real world demonstrations"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "transferring end to end visuomotor control from simulation to real world for a multi stage task"
    - "reinforcement learning for pivoting task"
    - "control of a quadrotor with 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)