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 "reinforcement learning when all actions are not always available"):
  Abstract: The Markov decision process (MDP) formulation used to model many real-world sequential decision making problems does not capture the setting where the set of available decisions (actions) at each time step is stochastic. Recently, the stochastic action set Markov decision process (SAS-MDP) formulation has been proposed, which captures the concept of a stochastic action set. In this paper we argue that existing RL algorithms for SAS-MDPs suffer from divergence issues, and present new algorithms for SAS-MDPs that incorporate variance reduction techniques unique to this setting, and provide conditions for their convergence. We conclude with experiments that demonstrate the practicality of our approaches using several tasks inspired by real-life use cases wherein the action set is stochastic.

Includes members:
  Member "the mirage of action dependent baselines in reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "merging deterministic policy gradient estimations with varied bias variance tradeoff for effective deep reinforcement learning")
  Member "variance reduction for policy gradient with action dependent factorized baselines":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep reinforcement learning for cyber security")
  Member "using large ensembles of control variates for variational inference":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a unified view of likelihood ratio and reparameterization gradients and an optimal importance sampling scheme")
  Member "planning and learning with stochastic action sets":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "caql continuous action q learning")
  Member "backpropagation through the void optimizing control variates for black box gradient estimation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "generalizable adversarial attacks with latent variable perturbation modelling")
  Member "policy gradient methods for reinforcement learning with function approximation and action dependent baselines":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "power allocation in multi user cellular networks deep reinforcement learning approaches")
  Member "online advertisement optimization and stochastic networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "throughput optimal routing in blockchain based payment systems")

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)