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 "policy optimization with stochastic mirror descent"):
  Abstract: Stochastic mirror descent (SMD) keeps the advantages of simplicity of implementation, low memory requirement, and low computational complexity. However, the non-convexity of objective function with its non-stationary sampling process is the main bottleneck of applying SMD to reinforcement learning. To address the above problem, we propose the mirror policy optimization (MPO) by estimating the policy gradient via dynamic batch-size of gradient information. Comparing with REINFORCE or VPG, the proposed MPO improves the convergence rate from $\mathcal{O}({{1}/{\sqrt{N}}})$ to $\mathcal{O}({\ln N}/{N})$. We also propose VRMPO algorithm, a variance reduction implementation of MPO. We prove the convergence of VRMPO and show its computational complexity. We evaluate the performance of VRMPO on the MuJoCo continuous control tasks, results show that VRMPO outperforms or matches several state-of-art algorithms DDPG, TRPO, PPO, and TD3.

Includes members:
  Member "an improved convergence analysis of stochastic variance reduced policy gradient":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "non asymptotic convergence of adam type reinforcement learning algorithms under markovian sampling")
  Member "optimality and approximation with policy gradient methods in markov decision processes":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "upper confidence primal dual optimization stochastically constrained markov decision processes with adversarial losses and unknown transitions")
  Member "trajectory wise control variates for variance reduction in policy gradient methods":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "from importance sampling to doubly robust policy gradient")
  Member "stochastic gradient mirror descent minimax optimality and implicit regularization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on the distance between two neural networks and the stability of learning")
  Member "variance reduction for policy gradient with action dependent factorized baselines":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep reinforcement learning for cyber security")
  Member "addressing function approximation error in actor critic methods":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "self supervised discovering of causal features towards interpretable reinforcement learning")
  Member "predictor corrector policy optimization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "imitation projected programmatic 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)