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 "stochastic variance reduced policy gradient"):
  Abstract: In this paper, we propose a novel reinforcement- learning algorithm consisting in a stochastic variance-reduced version of policy gradient for solving Markov Decision Processes (MDPs). Stochastic variance-reduced gradient (SVRG) methods have proven to be very successful in supervised learning. However, their adaptation to policy gradient is not straightforward and needs to account for I) a non-concave objective func- tion; II) approximations in the full gradient com- putation; and III) a non-stationary sampling pro- cess. The result is SVRPG, a stochastic variance- reduced policy gradient algorithm that leverages on importance weights to preserve the unbiased- ness of the gradient estimate. Under standard as- sumptions on the MDP, we provide convergence guarantees for SVRPG with a convergence rate that is linear under increasing batch sizes. Finally, we suggest practical variants of SVRPG, and we empirically evaluate them on continuous MDPs.

Includes members:
  Member "policy gradient methods for reinforcement learning with function approximation and action dependent baselines":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "power allocation in multi user cellular networks deep reinforcement learning approaches")
  Member "stochastic variance reduction for policy gradient estimation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "policy optimization with stochastic mirror descent")
  Member "stochastic variance reduction methods for policy evaluation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "provably convergent two timescale off policy actor critic with function approximation")
  Member "stochastic variance reduction methods for saddle point problems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "accelerating smooth games by manipulating spectral shapes")
  Member "mini batch semi stochastic gradient descent in the proximal setting":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "dynamic mini batch sgd for elastic distributed training learning in the limbo of resources")
  Member "benchmarking deep reinforcement learning for continuous control":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "review analyze and design a comprehensive deep reinforcement learning framework")
  Member "stop wasting my gradients practical svrg":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "sample efficient policy gradient methods with recursive variance reduction")
  Member "adam a method for stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (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.LG(Machine Learning)