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: "fourier policy gradients"
  Abstract: We propose a new way of deriving policy gradient updates for reinforcement learning. Our technique, based on Fourier analysis, recasts integrals that arise with expected policy gradients as convolutions and turns them into multiplications. The obtained analytical solutions allow us to capture the low variance benefits of EPG in a broad range of settings. For the critic, we treat trigonometric and radial basis functions, two function families with the universal approximation property. The choice of policy can be almost arbitrary, including mixtures or hybrid continuous-discrete probability distributions. Moreover, we derive a general family of sample-based estimators for stochastic policy gradients, which unifies existing results on sample-based approximation. We believe that this technique has the potential to shape the next generation of policy gradient approaches, powered by analytical results.

Connected through hyperedge HE-1 (induced by source paper "provably efficient reconstruction of policy networks"):
  with members:
    - "safe policy improvement with an estimated baseline policy"
    - "attraction repulsion actor critic for continuous control reinforcement learning"
    - "leveraging exploration in off policy algorithms via normalizing flows"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "on a formal model of safe and scalable self driving cars"
    - "fully adaptive feature sharing in multi task networks with applications in person attribute classification"
    - "continuous control with deep reinforcement learning"
    - "trust region policy optimization"

Connected through hyperedge HE-2 (induced by source paper "all action policy gradient methods a numerical integration approach"):
  with members:
    - "off policy policy gradient with state distribution correction"
    - "simple random search provides a competitive approach to reinforcement learning"
    - "gradient estimation using stochastic computation graphs"
    - "off policy actor critic"
    - "infinite horizon policy gradient estimation"

Connected through hyperedge HE-3 (induced by source paper "virel a variational inference framework for reinforcement learning"):
  with members:
    - "a theoretical analysis of deep q learning"
    - "a theory of regularized markov decision processes"
    - "maven multi agent variational exploration"
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "addressing function approximation error in actor critic methods"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "dice the infinitely differentiable monte carlo estimator"
    - "equivalence between policy gradients and soft q learning"

Connected through hyperedge HE-4 (induced by source paper "marginal policy gradients a unified family of estimators for bounded action spaces with applications"):
  with members:
    - "clipped action policy gradient"
    - "self normalizing neural networks"
    - "starcraft ii a new challenge for reinforcement learning"
    - "stochastic neural networks for hierarchical reinforcement learning"
    - "proximal policy optimization algorithms"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "asynchronous methods for deep reinforcement learning"
    - "episodic exploration for deep deterministic policies an application to starcraft micromanagement tasks"

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)