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 "robel robotics benchmarks for learning with low cost robots"):
  Abstract: ROBEL is an open-source platform of cost-effective robots designed for reinforcement learning in the real world. ROBEL introduces two robots, each aimed to accelerate reinforcement learning research in different task domains: D'Claw is a three-fingered hand robot that facilitates learning dexterous manipulation tasks, and D'Kitty is a four-legged robot that facilitates learning agile legged locomotion tasks. These low-cost, modular robots are easy to maintain and are robust enough to sustain on-hardware reinforcement learning from scratch with over 14000 training hours registered on them to date. To leverage this platform, we propose an extensible set of continuous control benchmark tasks for each robot. These tasks feature dense and sparse task objectives, and additionally introduce score metrics as hardware-safety. We provide benchmark scores on an initial set of tasks using a variety of learning-based methods. Furthermore, we show that these results can be replicated across copies of the robots located in different institutions. Code, documentation, design files, detailed assembly instructions, final policies, baseline details, task videos, and all supplementary materials required to reproduce the results are available at www.roboticsbenchmarks.org.

Includes members:
  Member "replab a reproducible low cost arm benchmark platform for robotic learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "rigid soft interactive learning for robust grasping")
  Member "active domain randomization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "automatic curriculum learning for deep rl a short survey")
  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 "stochastic latent actor critic deep reinforcement learning with a latent variable model":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "interpretable end to end urban autonomous driving with latent deep reinforcement learning")
  Member "qt opt scalable deep reinforcement learning for vision based robotic manipulation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "probabilistic future prediction for video scene understanding")
  Member "deepmind control suite":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "learning dexterous in hand manipulation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "never give up learning directed exploration strategies")
  Member "dexterous manipulation with deep reinforcement learning efficient general and low cost":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "in hand object pose tracking via contact feedback and gpu accelerated robotic simulation")

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)