human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "gym ignition reproducible robotic simulations for reinforcement learning"):
  Abstract: In this paper we present Gym-Ignition, a new framework to create reproducible robotic environments for reinforcement learning research. It interfaces with the new generation of Gazebo, part of the Ignition Robotics suite. The new Ignition Gazebo simulator mainly provides three improvements for reinforcement learning applications compared to the alternatives: 1) the modular architecture enables using the simulator as a C++ library, simplifying the interconnection with external software; 2) multiple physics and rendering engines are supported as plugins, and they can be switched during runtime; 3) the new distributed simulation capability permits simulating complex scenarios while sharing the load on multiple workers and machines. The core of Gym-Ignition is a component that contains the Ignition Gazebo simulator, and it simplifies its configuration and usage. We provide a Python package that permits developers to create robotic environments simulated in Ignition Gazebo. Environments expose the common OpenAI Gym interface, making them compatible out-of-the-box with third-party frameworks containing reinforcement learning algorithms. Simulations can be executed in both headless and GUI mode, and the physics engine can run in accelerated mode and instances can be parallelized. Furthermore, the Gym-Ignition software architecture provides abstraction of the Robot and the Task, making environments agnostic from the specific runtime. This allows their execution also in a real-time setting on actual robotic platforms.

Includes members:
  Member "modelling generalized forces with reinforcement learning for sim to real transfer":
    also connected through other hyperedges:
      (none)
  Member "hierarchical reinforcement learning for quadruped locomotion":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multi agent manipulation via locomotion using hierarchical sim2real")
  Member "assessing transferability from simulation to reality for reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep reinforcement learning with linear quadratic regulator regions")
  Member "bayessim adaptive domain randomization via probabilistic inference for robotics simulators":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "disco double likelihood free inference stochastic control")
  Member "iterative reinforcement learning based design of dynamic locomotion skills for cassie":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning to manipulate object collections using grounded state representations")
  Member "gym gazebo2 a toolkit for reinforcement learning using ros 2 and gazebo":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "ros2learn a reinforcement learning framework for ros 2")
  Member "learning agile and dynamic motor skills for legged robots":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "rapidly adaptable legged robots via evolutionary meta learning")
  Member "deepmind control suite":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")

Question: Please tell me which class the source paper 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)