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: "extending the openai gym for robotics a toolkit for reinforcement learning using ros and gazebo"
  Abstract: This paper presents an extension of the OpenAI Gym for robotics using the Robot Operating System (ROS) and the Gazebo simulator. The content discusses the software architecture proposed and the results obtained by using two Reinforcement Learning techniques: Q-Learning and Sarsa. Ultimately, the output of this work presents a benchmarking system for robotics that allows different techniques and algorithms to be compared using the same virtual conditions.

Connected through hyperedge HE-1 (induced by source paper "danger aware adaptive composition of drl agents for self navigation"):
  with members:
    - "multi advisor reinforcement learning"
    - "learning with training wheels speeding up training with a simple controller for deep reinforcement learning"
    - "utility decomposition with deep corrections for scalable planning under uncertainty"
    - "obstacle avoidance through deep networks based intermediate perception"
    - "towards monocular vision based obstacle avoidance through deep reinforcement learning"
    - "feudal networks for hierarchical reinforcement learning"
    - "socially aware motion planning with deep reinforcement learning"
    - "uncertainty aware reinforcement learning for collision avoidance"

Connected through hyperedge HE-2 (induced by source paper "the chef s hat simulation environment for reinforcement learning based agents"):
  with members:
    - "it s food fight introducing the chef s hat card game for affective aware hri"
    - "deep reinforcement learning for general video game ai"
    - "qualitative measurements of policy discrepancy for return based deep q network"
    - "deep q learning from demonstrations"
    - "proximal policy optimization algorithms"
    - "deep recurrent q learning for partially observable mdps"
    - "deep reinforcement learning with double q learning"

Connected through hyperedge HE-3 (induced by source paper "offworld gym open access physical robotics environment for real world reinforcement learning benchmark and research"):
  with members:
    - "replab a reproducible low cost arm benchmark platform for robotic learning"
    - "learning latent state representation for speeding up exploration"
    - "go explore a new approach for hard exploration problems"
    - "pyrep bringing v rep to deep robot learning"
    - "self supervised exploration via disagreement"
    - "provably efficient imitation learning from observation alone"
    - "quasi direct drive for low cost compliant robotic manipulation"
    - "challenges of real world reinforcement learning"

Connected through hyperedge HE-4 (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-5 (induced by source paper "assistive gym a physics simulation framework for assistive robotics"):
  with members:
    - "multidimensional capacitive sensing for robot assisted dressing and bathing"
    - "modeling collaboration for robot assisted dressing tasks"
    - "tossingbot learning to throw arbitrary objects with residual physics"
    - "habitat a platform for embodied ai research"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "sim to real learning agile locomotion for quadruped robots"
    - "deep haptic model predictive control for robot assisted dressing"
    - "multi goal reinforcement learning challenging robotics environments and request for research"

Connected through hyperedge HE-6 (induced by source paper "flight controller synthesis via deep reinforcement learning"):
  with members:
    - "air learning an ai research platform for algorithm hardware benchmarking of autonomous aerial robots"
    - "neuroflight next generation flight control firmware"
    - "sim to multi real transfer of low level robust control policies to multiple quadrotors"
    - "learning dexterous in hand manipulation"
    - "reinforcement learning for uav attitude control"
    - "end to end analysis and design of a drone flight controller"
    - "bohb robust and efficient hyperparameter optimization at scale"
    - "deep reinforcement learning that matters"

Connected through hyperedge HE-7 (induced by source paper "addressing sample complexity in visual tasks using her and hallucinatory gans"):
  with members:
    - "generative predecessor models for sample efficient imitation learning"
    - "imitating latent policies from observation"
    - "visual reinforcement learning with imagined goals"
    - "forward backward reinforcement learning"
    - "world models"
    - "few shot goal inference for visuomotor learning and planning"
    - "large scale gan training for high fidelity natural image synthesis"
    - "hindsight experience replay"

Connected through hyperedge HE-8 (induced by source paper "visual hindsight experience replay"):
  with members:
    - "imitating latent policies from observation"
    - "learning dexterous in hand manipulation"
    - "visual reinforcement learning with imagined goals"
    - "forward backward reinforcement learning"
    - "world models"
    - "few shot goal inference for visuomotor learning and planning"
    - "hindsight experience replay"
    - "automatic goal generation for reinforcement learning agents"

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)