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 "trajectory wise control variates for variance reduction in policy gradient methods"):
  Abstract: Policy gradient methods have demonstrated success in reinforcement learning tasks that have high-dimensional continuous state and action spaces. However, policy gradient methods are also notoriously sample inefficient. This can be attributed, at least in part, to the high variance in estimating the gradient of the task objective with Monte Carlo methods. Previous research has endeavored to contend with this problem by studying control variates (CVs) that can reduce the variance of estimates without introducing bias, including the early use of baselines, state dependent CVs, and the more recent state-action dependent CVs. In this work, we analyze the properties and drawbacks of previous CV techniques and, surprisingly, we find that these works have overlooked an important fact that Monte Carlo gradient estimates are generated by trajectories of states and actions. We show that ignoring the correlation across the trajectories can result in suboptimal variance reduction, and we propose a simple fix: a class of "trajectory-wise" CVs, that can further drive down the variance. We show that constructing trajectory-wise CVs can be done recursively and requires only learning state-action value functions like the previous CVs for policy gradient. We further prove that the proposed trajectory-wise CVs are optimal for variance reduction under reasonable assumptions.

Includes members:
  Member "policy optimization with stochastic mirror descent":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a hybrid stochastic policy gradient algorithm for reinforcement learning")
  Member "contrasting exploration in parameter and action space a zeroth order optimization perspective":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sample complexity of estimating the policy gradient for nearly deterministic dynamical systems")
  Member "truncated horizon policy search combining reinforcement learning imitation learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "exploration efficient deep reinforcement learning with demonstration guidance for robot control")
  Member "the mirage of action dependent baselines in reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "merging deterministic policy gradient estimations with varied bias variance tradeoff for effective deep reinforcement learning")
  Member "reward estimation variance elimination in sequential decision processes":
    also connected through other hyperedges:
      (none)
  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 "beyond the one step greedy approach in reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "multi step greedy policies in model free deep reinforcement learning")
  Member "predictor corrector policy optimization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "imitation projected programmatic reinforcement learning")

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)