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: "3d simulation for robot arm control with deep q learning"
  Abstract: Recent trends in robot arm control have seen a shift towards end-to-end solutions, using deep reinforcement learning to learn a controller directly from raw sensor data, rather than relying on a hand-crafted, modular pipeline. However, the high dimensionality of the state space often means that it is impractical to generate sufficient training data with real-world experiments. As an alternative solution, we propose to learn a robot controller in simulation, with the potential of then transferring this to a real robot. Building upon the recent success of deep Q-networks, we present an approach which uses 3D simulations to train a 7-DOF robotic arm in a control task without any prior knowledge. The controller accepts images of the environment as its only input, and outputs motor actions for the task of locating and grasping a cube, over a range of initial configurations. To encourage efficient learning, a structured reward function is designed with intermediate rewards. We also present preliminary results in direct transfer of policies over to a real robot, without any further training.

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 "memristor hardware friendly reinforcement learning"):
  with members:
    - "parameter space noise for exploration"
    - "proximal policy optimization algorithms"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"
    - "deep reinforcement learning for robotic manipulation with asynchronous off policy updates"
    - "continuous control with deep reinforcement learning"
    - "trust region policy optimization"
    - "training and operation of an integrated neuromorphic network based on metal oxide memristors"
    - "design of a hybrid robot control system using memristor model and ant inspired based information transfer protocols"

Connected through hyperedge HE-3 (induced by source paper "rlbench the robot learning benchmark learning environment"):
  with members:
    - "pyrep bringing v rep to deep robot learning"
    - "benchmarking simulated robotic manipulation through a real world dataset"
    - "meta world a benchmark and evaluation for multi task and meta reinforcement learning"
    - "learning agile and dynamic motor skills for legged robots"
    - "simitate a hybrid imitation learning benchmark"
    - "robonet large scale multi robot learning"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "deepmind control suite"

Connected through hyperedge HE-4 (induced by source paper "learning one shot imitation from humans without humans"):
  with members:
    - "pyrep bringing v rep to deep robot learning"
    - "sim to real reinforcement learning for deformable object manipulation"
    - "deep episodic memory encoding recalling and predicting episodic experiences for robot action execution"
    - "sim to real via sim to sim data efficient robotic grasping via randomized to canonical adaptation networks"
    - "task embedded control networks for few shot imitation learning"
    - "one shot imitation from observing humans via domain adaptive meta learning"
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"
    - "one shot visual imitation learning via meta learning"

Connected through hyperedge HE-5 (induced by source paper "ros2learn a reinforcement learning framework for ros 2"):
  with members:
    - "gym gazebo2 a toolkit for reinforcement learning using ros 2 and gazebo"
    - "meta learning shared hierarchies"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "deep predictive policy training using reinforcement learning"
    - "dissecting robotics historical overview and future perspectives"
    - "one shot imitation learning"
    - "proximal policy optimization algorithms"

Connected through hyperedge HE-6 (induced by source paper "the limits and potentials of deep learning for robotics"):
  with members:
    - "psyphy a psychophysics driven evaluation framework for visual recognition"
    - "on calibration of modern neural networks"
    - "domain adaptation for visual applications a comprehensive survey"
    - "progressive neural networks"
    - "core50 a new dataset and benchmark for continuous object recognition"
    - "se3 pose nets structured deep dynamics models for visuomotor planning and control"
    - "sparse representation based open set recognition"
    - "physical problem solving joint planning with symbolic geometric and dynamic constraints"

Connected through hyperedge HE-7 (induced by source paper "sim to real via sim to sim data efficient robotic grasping via randomized to canonical adaptation networks"):
  with members:
    - "vr goggles for robots real to sim domain adaptation for visual control"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "the limits and potentials of deep learning for robotics"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "sim to real reinforcement learning for deformable object manipulation"
    - "learning synergies between pushing and grasping with self supervised deep reinforcement learning"
    - "closing the sim to real loop adapting simulation randomization with real world experience"
    - "task embedded control networks for few shot imitation learning"

Connected through hyperedge HE-8 (induced by source paper "beyond pixel norm balls parametric adversaries using an analytically differentiable renderer"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "benchmarking neural network robustness to common corruptions and surface variations"
    - "training augmentation with adversarial examples for robust speech recognition"
    - "unsupervised training for 3d morphable model regression"
    - "training deep networks with synthetic data bridging the reality gap by domain randomization"
    - "defense against the dark arts an overview of adversarial example security research and future research directions"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "motivating the rules of the game for adversarial example research"

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)