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: "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.

Connected through hyperedge HE-1 (induced by source paper "a hybrid stochastic policy gradient algorithm for reinforcement learning"):
  with members:
    - "an improved convergence analysis of stochastic variance reduced policy gradient"
    - "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-2 (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"
    - "an improved convergence analysis of stochastic variance reduced policy gradient"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "trajectory wise control variates for variance reduction in policy gradient methods"):
  with members:
    - "contrasting exploration in parameter and action space a zeroth order optimization perspective"
    - "truncated horizon policy search combining reinforcement learning imitation learning"
    - "the mirage of action dependent baselines in reinforcement learning"
    - "reward estimation variance elimination in sequential decision processes"
    - "variance reduction for policy gradient with action dependent factorized baselines"
    - "beyond the one step greedy approach in reinforcement learning"
    - "predictor corrector policy optimization"
    - "fast policy learning through imitation and reinforcement"

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)