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: "gpu accelerated robotic simulation for distributed reinforcement learning"
  Abstract: Most Deep Reinforcement Learning (Deep RL) algorithms require a prohibitively large number of training samples for learning complex tasks. Many recent works on speeding up Deep RL have focused on distributed training and simulation. While distributed training is often done on the GPU, simulation is not. In this work, we propose using GPU-accelerated RL simulations as an alternative to CPU ones. Using NVIDIA Flex, a GPU-based physics engine, we show promising speed-ups of learning various continuous-control, locomotion tasks. With one GPU and CPU core, we are able to train the Humanoid running task in less than 20 minutes, using 10-1000x fewer CPU cores than previous works. We also demonstrate the scalability of our simulator to multi-GPU settings to train more challenging locomotion tasks.

Connected through hyperedge HE-1 (induced by source paper "population guided parallel policy search for reinforcement learning"):
  with members:
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "structured evolution with compact architectures for scalable policy optimization"
    - "exploration by random network distillation"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "cem rl combining evolutionary and gradient based methods for policy search"
    - "scalable coordinated exploration in concurrent reinforcement learning"
    - "self imitation learning"
    - "coordinated exploration in concurrent reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "in hand object pose tracking via contact feedback and gpu accelerated robotic simulation"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "learning to estimate pose and shape of hand held objects from rgb images"
    - "poserbpf a rao blackwellized particle filter for 6d object pose tracking"
    - "learning joint reconstruction of hands and manipulated objects"
    - "joint inference of kinematic and force trajectories with visuo tactile sensing"
    - "making sense of vision and touch learning multimodal representations for contact rich tasks"
    - "non smooth newton methods for deformable multi body dynamics"
    - "learning dexterous in hand manipulation"

Connected through hyperedge HE-3 (induced by source paper "inferring the material properties of granular media for robotic tasks"):
  with members:
    - "bayessim adaptive domain randomization via probabilistic inference for robotics simulators"
    - "non smooth newton methods for deformable multi body dynamics"
    - "closing the sim to real loop adapting simulation randomization with real world experience"
    - "learning robotic manipulation of granular media"

Connected through hyperedge HE-4 (induced by source paper "deepracer educational autonomous racing platform for experimentation with sim2real reinforcement learning"):
  with members:
    - "deep drone racing from simulation to reality with domain randomization"
    - "f1 10 an open source autonomous cyber physical platform"
    - "mushr a low cost open source robotic racecar for education and research"
    - "quasi direct drive for low cost compliant robotic manipulation"
    - "self driving scale car trained by deep reinforcement learning"
    - "an online learning approach to model predictive control"
    - "learning agile and dynamic motor skills for legged robots"
    - "demonstration guided deep reinforcement learning of control policies for dexterous human robot interaction"

Connected through hyperedge HE-5 (induced by source paper "meta reinforcement learning for sim to real domain adaptation"):
  with members:
    - "affordance learning for end to end visuomotor robot control"
    - "feedback based fabric strip folding"
    - "some considerations on learning to explore via meta reinforcement learning"
    - "learning dexterous in hand manipulation"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "differentiable plasticity training plastic neural networks with backpropagation"
    - "sim to real learning agile locomotion for quadruped robots"
    - "how to train your maml"

Connected through hyperedge HE-6 (induced by source paper "gym ignition reproducible robotic simulations for reinforcement learning"):
  with members:
    - "modelling generalized forces with reinforcement learning for sim to real transfer"
    - "hierarchical reinforcement learning for quadruped locomotion"
    - "assessing transferability from simulation to reality for reinforcement learning"
    - "bayessim adaptive domain randomization via probabilistic inference for robotics simulators"
    - "iterative reinforcement learning based design of dynamic locomotion skills for cassie"
    - "gym gazebo2 a toolkit for reinforcement learning using ros 2 and gazebo"
    - "learning agile and dynamic motor skills for legged robots"
    - "deepmind control suite"

Connected through hyperedge HE-7 (induced by source paper "titan a parallel asynchronous library for multi agent and soft body robotics using nvidia cuda"):
  with members:
    - "chainqueen a real time differentiable physical simulator for soft robotics"
    - "carla an open urban driving simulator"

Connected through hyperedge HE-8 (induced by source paper "closing the sim to real loop adapting simulation randomization with real world experience"):
  with members:
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "learning dexterous in hand manipulation"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "sim to real learning agile locomotion for quadruped robots"
    - "reinforcement learning for non prehensile manipulation transfer from simulation to physical system"
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"
    - "multi modal imitation learning from unstructured demonstrations using generative adversarial nets"
    - "mutual alignment transfer 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)