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: "sim to real transfer of accurate grasping with eye in hand observations and continuous control"
  Abstract: In the context of deep learning for robotics, we show effective method of training a real robot to grasp a tiny sphere (1.37cm of diameter), with an original combination of system design choices. We decompose the end-to-end system into a vision module and a closed-loop controller module. The two modules use target object segmentation as their common interface. The vision module extracts information from the robot end-effector camera, in the form of a binary segmentation mask of the target. We train it to achieve effective domain transfer by composing real background images with simulated images of the target. The controller module takes as input the binary segmentation mask, and thus is agnostic to visual discrepancies between simulated and real environments. We train our closed-loop controller in simulation using imitation learning and show it is robust with respect to discrepancies between the dynamic model of the simulated and real robot: when combined with eye-in-hand observations, we achieve a 90% success rate in grasping a tiny sphere with a real robot. The controller can generalize to unseen scenarios where the target is moving and even learns to recover from failures.

Connected through hyperedge HE-1 (induced by source paper "toward sim to real directional semantic grasping"):
  with members:
    - "mechanical search multi step retrieval of a target object occluded by clutter"
    - "deep reinforcement learning for vision based robotic grasping a simulated comparative evaluation of off policy methods"
    - "learning task oriented grasping for tool manipulation from simulated self supervision"
    - "deep object pose estimation for semantic robotic grasping of household objects"
    - "sim to real reinforcement learning for deformable object manipulation"
    - "unity a general platform for intelligent agents"
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"
    - "adversarial discriminative sim to real transfer of visuo motor policies"

Connected through hyperedge HE-2 (induced by source paper "sim2real transfer for reinforcement learning without dynamics randomization"):
  with members:
    - "solving rubik s cube with a robot hand"
    - "contact skill imitation learning for robot independent assembly programming"
    - "learning dexterous in hand manipulation"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "soft actor critic algorithms and applications"
    - "closing the sim to real loop adapting simulation randomization with real world experience"
    - "making sense of vision and touch self supervised learning of multimodal representations for contact rich tasks"
    - "deep reinforcement learning for high precision assembly tasks"

Connected through hyperedge HE-3 (induced by source paper "directional semantic grasping of real world objects from simulation to reality"):
  with members:
    - "deep reinforcement learning for vision based robotic grasping a simulated comparative evaluation of off policy methods"
    - "learning task oriented grasping for tool manipulation from simulated self supervision"
    - "sim to real reinforcement learning for deformable object manipulation"
    - "unity a general platform for intelligent agents"
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"
    - "adversarial discriminative sim to real transfer of visuo motor policies"
    - "multi task domain adaptation for deep learning of instance grasping from simulation"
    - "domain randomization for transferring deep neural networks from simulation to the real world"

Connected through hyperedge HE-4 (induced by source paper "a data efficient framework for training and sim to real transfer of navigation policies"):
  with members:
    - "universal planning networks"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "bayesian model agnostic meta learning"
    - "sim to real learning agile locomotion for quadruped robots"
    - "evolved policy gradients"
    - "probabilistic model agnostic meta learning"
    - "adversarial discriminative sim to real transfer of visuo motor policies"

Connected through hyperedge HE-5 (induced by source paper "virtual to real learning to control in visual semantic segmentation"):
  with members:
    - "sim to real transfer of robotic control with dynamics randomization"
    - "adversarial discriminative sim to real transfer of visuo motor policies"
    - "virtual to real reinforcement learning for autonomous driving"
    - "no more discrimination cross city adaptation of road scene segmenters"
    - "toward low flying autonomous mav trail navigation using deep neural networks for environmental awareness"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "neural map structured memory for deep reinforcement learning"
    - "icnet for real time semantic segmentation on high resolution images"

Connected through hyperedge HE-6 (induced by source paper "transfer learning from synthetic to real images using variational autoencoders for precise position detection"):
  with members:
    - "sim to real transfer of robotic control with dynamics randomization"
    - "adversarial discriminative sim to real transfer of visuo motor policies"
    - "learning invariant feature spaces to transfer skills with reinforcement learning"
    - "visual servoing from deep neural networks"
    - "learning a driving simulator"
    - "learning from simulated and unsupervised images through adversarial training"
    - "learning deep object detectors from 3d models"

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)