human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "an improved convergence analysis of stochastic variance reduced policy gradient"
  Abstract: We revisit the stochastic variance-reduced policy gradient (SVRPG) method proposed by Papini et al. (2018) for reinforcement learning. We provide an improved convergence analysis of SVRPG and show that it can find an $\epsilon$-approximate stationary point of the performance function within $O(1/\epsilon^{5/3})$ trajectories. This sample complexity improves upon the best known result $O(1/\epsilon^2)$ by a factor of $O(1/\epsilon^{1/3})$. At the core of our analysis is (i) a tighter upper bound for the variance of importance sampling weights, where we prove that the variance can be controlled by the parameter distance between different policies; and (ii) a fine-grained analysis of the epoch length and batch size parameters such that we can significantly reduce the number of trajectories required in each iteration of SVRPG. We also empirically demonstrate the effectiveness of our theoretical claims of batch sizes on reinforcement learning benchmark tasks.

Connected through hyperedge HE-1 (induced by source paper "non asymptotic convergence of adam type reinforcement learning algorithms under markovian sampling"):
  with members:
    - "finite time error bounds for linear stochastic approximation and td learning"
    - "two time scale off policy td learning non asymptotic analysis over markovian samples"
    - "on the convergence proof of amsgrad and a new version"
    - "smoothing policies and safe policy gradients"
    - "finite sample analysis for sarsa with linear function approximation"
    - "characterizing the exact behaviors of temporal difference learning algorithms using markov jump linear system theory"
    - "neural temporal difference learning converges to global optima"
    - "global optimality guarantees for policy gradient methods"

Connected through hyperedge HE-2 (induced by source paper "generative adversarial imitation learning with neural networks global optimality and convergence rate"):
  with members:
    - "on computation and generalization of generative adversarial imitation learning"
    - "provably efficient reinforcement learning with linear function approximation"
    - "neural proximal trust region policy optimization attains globally optimal policy"
    - "sample optimal parametric q learning using linearly additive features"
    - "optimality and approximation with policy gradient methods in markov decision processes"
    - "on the global convergence of imitation learning a case for linear quadratic regulator"
    - "policy optimization provably converges to nash equilibria in zero sum linear quadratic games"
    - "provably efficient exploration in policy optimization"

Connected through hyperedge HE-3 (induced by source paper "a hybrid stochastic policy gradient algorithm for reinforcement learning"):
  with members:
    - "policy optimization with stochastic mirror descent"
    - "regularization matters in policy optimization"
    - "momentum based variance reduction in non convex sgd"
    - "sample efficient policy gradient methods with recursive variance reduction"
    - "stochastic nested variance reduction for nonconvex optimization"
    - "stochastic variance reduced policy gradient"
    - "exploring generalization in deep learning"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"

Connected through hyperedge HE-4 (induced by source paper "neural policy gradient methods global optimality and rates of convergence"):
  with members:
    - "neural proximal trust region policy optimization attains globally optimal policy"
    - "a theoretical analysis of deep q learning"
    - "optimality and approximation with policy gradient methods in markov decision processes"
    - "information theoretic considerations in batch reinforcement learning"
    - "on the global convergence of actor critic a case for linear quadratic regulator with ergodic cost"
    - "neural temporal difference learning converges to global optima"
    - "global optimality guarantees for policy gradient methods"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"

Connected through hyperedge HE-5 (induced by source paper "policy optimization with stochastic mirror descent"):
  with members:
    - "optimality and approximation with policy gradient methods in markov decision processes"
    - "trajectory wise control variates for variance reduction in policy gradient methods"
    - "stochastic gradient mirror descent minimax optimality and implicit regularization"
    - "variance reduction for policy gradient with action dependent factorized baselines"
    - "addressing function approximation error in actor critic methods"
    - "predictor corrector policy optimization"
    - "stochastic variance reduced policy gradient"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"

Connected through hyperedge HE-6 (induced by source paper "sample efficient policy gradient methods with recursive variance reduction"):
  with members:
    - "neural proximal trust region policy optimization attains globally optimal policy"
    - "policy optimization with stochastic mirror descent"
    - "neural temporal difference learning converges to global optima"
    - "neural policy gradient methods global optimality and rates of convergence"
    - "a generalization theory of gradient descent for learning over parameterized deep relu networks"
    - "the mirage of action dependent baselines in reinforcement learning"
    - "variance reduction for policy gradient with action dependent factorized baselines"
    - "gradient descent finds global minima of deep neural networks"

Question: Please tell me which class the center node 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)