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 "smoothing policies and safe policy gradients"):
  Abstract: Policy gradient algorithms are among the best candidates for the much anticipated application of reinforcement learning to real-world control tasks, such as the ones arising in robotics. However, the trial-and-error nature of these methods introduces safety issues whenever the learning phase itself must be performed on a physical system. In this paper, we address a specific safety formulation, where danger is encoded in the reward signal and the learning agent is constrained to never worsen its performance. By studying actor-only policy gradient from a stochastic optimization perspective, we establish improvement guarantees for a wide class of parametric policies, generalizing existing results on Gaussian policies. This, together with novel upper bounds on the variance of policy gradient estimators, allows to identify those meta-parameter schedules that guarantee monotonic improvement with high probability. The two key meta-parameters are the step size of the parameter updates and the batch size of the gradient estimators. By a joint, adaptive selection of these meta-parameters, we obtain a safe policy gradient algorithm.

Includes members:
  Member "compatible natural gradient policy search":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a survey of optimization methods from a machine learning perspective")
  Member "diverse exploration for fast and safe policy improvement":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning transferable domain priors for safe exploration in reinforcement learning")
  Member "impala scalable distributed deep rl with importance weighted actor learner architectures":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "explore discover and learn unsupervised discovery of state covering skills")
  Member "variance reduction for policy gradient with action dependent factorized baselines":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep reinforcement learning for cyber security")
  Member "learning dexterous in hand manipulation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "never give up learning directed exploration strategies")
  Member "sim to real learning agile locomotion for quadruped robots":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning to fly via deep model based reinforcement learning")
  Member "a lyapunov based approach to safe reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "constrained upper confidence reinforcement learning")
  Member "stochastic variance reduced policy gradient":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "non asymptotic convergence of adam type reinforcement learning algorithms under markovian sampling")

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)