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: "learning a visuomotor controller for real world robotic grasping using easily simulated depth images"
  Abstract: We want to build robots that are useful in unstructured real world applications, such as doing work in the household. Grasping in particular is an important skill in this domain, yet it remains a challenge. One of the key hurdles is handling unexpected changes or motion in the objects being grasped and kinematic noise or other errors in the robot. This paper proposes an approach to learning a closed-loop controller for robotic grasping that dynamically guides the gripper to the object. We use a wrist-mounted sensor to acquire depth images in front of the gripper and train a convolutional neural network to learn a distance function to true grasps for grasp configurations over an image. The training sensor data is generated in simulation, a major advantage over previous work that uses real robot experience, which is costly to obtain. Despite being trained in simulation, our approach works well on real noisy sensor images. We compare our controller in simulated and real robot experiments to a strong baseline for grasp pose detection, and find that our approach significantly outperforms the baseline in the presence of kinematic noise, perceptual errors and disturbances of the object during grasping.

Connected through hyperedge HE-1 (induced by source paper "domain independent unsupervised learning to grasp the novel objects"):
  with members:
    - "robotic grasp detection using deep convolutional neural networks"
    - "high precision grasp pose detection in dense clutter"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"
    - "using geometry to detect grasps in 3d point clouds"
    - "supersizing self supervision learning to grasp from 50k tries and 700 robot hours"
    - "real time grasp detection using convolutional neural networks"
    - "data driven grasp synthesis a survey"

Connected through hyperedge HE-2 (induced by source paper "vision based robotic grasping from object localization pose estimation grasp detection to motion planning a review"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "pixor real time 3d object detection from point clouds"
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "densefusion 6d object pose estimation by iterative dense fusion"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "bop benchmark for 6d object pose estimation"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "attention privileged reinforcement learning for domain transfer"):
  with members:
    - "attention privileged reinforcement learning"
    - "solving rubik s cube with a robot hand"
    - "simultaneously learning vision and feature based control policies for real world ball in a cup"
    - "towards interpretable reinforcement learning using attention augmented agents"
    - "deepmind control suite"
    - "learning dexterous in hand manipulation"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "sim to real via sim to sim data efficient robotic grasping via randomized to canonical adaptation networks"

Connected through hyperedge HE-4 (induced by source paper "mechanical search multi step retrieval of a target object occluded by clutter"):
  with members:
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "learning task oriented grasping for tool manipulation from simulated self supervision"
    - "closing the loop for robotic grasping a real time generative grasp synthesis approach"
    - "one shot segmentation in clutter"
    - "segmenting unknown 3d objects from real depth images using mask r cnn trained on synthetic data"
    - "visual representations for semantic target driven navigation"
    - "randomized physics based motion planning for grasping in cluttered and uncertain environments"
    - "interactive perception leveraging action in perception and perception in action"

Connected through hyperedge HE-5 (induced by source paper "pixel attentive policy gradient for multi fingered grasping in cluttered scenes"):
  with members:
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "deep reinforcement learning for vision based robotic grasping a simulated comparative evaluation of off policy methods"
    - "learning synergies between pushing and grasping with self supervised deep reinforcement learning"
    - "sim to real via sim to sim data efficient robotic grasping via randomized to canonical adaptation networks"
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "proximal policy optimization algorithms"
    - "feature pyramid networks for object detection"

Connected through hyperedge HE-6 (induced by source paper "object pose estimation in robotics revisited"):
  with members:
    - "deep model based 6d pose refinement in rgb"
    - "bop benchmark for 6d object pose estimation"
    - "performance evaluation of 3d correspondence grouping algorithms"
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"
    - "t less an rgb d dataset for 6d pose estimation of texture less objects"
    - "ssd 6d making rgb based 3d detection and 6d pose estimation great again"
    - "high precision grasp pose detection in dense clutter"
    - "recovering 6d object pose and predicting next best view in the crowd"

Connected through hyperedge HE-7 (induced by source paper "morphology agnostic visual robotic control"):
  with members:
    - "replab a reproducible low cost arm benchmark platform for robotic learning"
    - "sim to real via sim to sim data efficient robotic grasping via randomized to canonical adaptation networks"
    - "contingency aware exploration in reinforcement learning"
    - "towards a generic diver following algorithm balancing robustness and efficiency in deep visual detection"
    - "learning visual servoing with deep features and fitted q iteration"
    - "self supervised visual planning with temporal skip connections"
    - "underwater multi robot convoying using visual tracking by detection"
    - "sim2real view invariant visual servoing by recurrent control"

Connected through hyperedge HE-8 (induced by source paper "reinforcement and imitation learning for diverse visuomotor skills"):
  with members:
    - "sim to real transfer of robotic control with dynamics randomization"
    - "data efficient deep reinforcement learning for dexterous manipulation"
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"
    - "leveraging demonstrations for deep reinforcement learning on robotics problems with sparse rewards"
    - "one shot visual imitation learning via meta learning"
    - "imitation from observation learning to imitate behaviors from raw video via context translation"
    - "progressive neural networks"
    - "domain randomization for transferring deep neural networks from simulation to the real world"

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)