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 "learning to walk via deep reinforcement learning"):
  Abstract: Deep reinforcement learning (deep RL) holds the promise of automating the acquisition of complex controllers that can map sensory inputs directly to low-level actions. In the domain of robotic locomotion, deep RL could enable learning locomotion skills with minimal engineering and without an explicit model of the robot dynamics. Unfortunately, applying deep RL to real-world robotic tasks is exceptionally difficult, primarily due to poor sample complexity and sensitivity to hyperparameters. While hyperparameters can be easily tuned in simulated domains, tuning may be prohibitively expensive on physical systems, such as legged robots, that can be damaged through extensive trial-and-error learning. In this paper, we propose a sample-efficient deep RL algorithm based on maximum entropy RL that requires minimal per-task tuning and only a modest number of trials to learn neural network policies. We apply this method to learning walking gaits on a real-world Minitaur robot. Our method can acquire a stable gait from scratch directly in the real world in about two hours, without relying on any model or simulation, and the resulting policy is robust to moderate variations in the environment. We further show that our algorithm achieves state-of-the-art performance on simulated benchmarks with a single set of hyperparameters. Videos of training and the learned policy can be found on the project website.

Includes members:
  Member "learning agile and dynamic motor skills for legged robots":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "rapidly adaptable legged robots via evolutionary meta learning")
  Member "policies modulating trajectory generators":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multiplicative controller fusion a hybrid navigation strategy for deployment in unknown environments")
  Member "value constrained model free continuous control":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "safe reinforcement learning for probabilistic reachability and safety specifications a lyapunov based approach")
  Member "composable deep reinforcement learning for robotic manipulation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "improving generalization of reinforcement learning with minimax distributional soft actor critic")
  Member "progressive reinforcement learning with distillation for multi skilled motion control":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning to walk in the real world with minimal human effort")
  Member "reward constrained policy optimization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "constrained deep reinforcement learning for energy sustainable multi uav based random access iot networks with noma")
  Member "sim to real learning agile locomotion for quadruped robots":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning to fly via deep model based reinforcement learning")
  Member "soft actor critic algorithms and applications":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "ready policy one world building through active learning")

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.LG(Machine Learning)