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 "probability functional descent a unifying perspective on gans variational inference and reinforcement learning"):
  Abstract: The goal of this paper is to provide a unifying view of a wide range of problems of interest in machine learning by framing them as the minimization of functionals defined on the space of probability measures. In particular, we show that generative adversarial networks, variational inference, and actor-critic methods in reinforcement learning can all be seen through the lens of our framework. We then discuss a generic optimization algorithm for our formulation, called probability functional descent (PFD), and show how this algorithm recovers existing methods developed independently in the settings mentioned earlier.

Includes members:
  Member "policy optimization as wasserstein gradient flows":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "regularizing activations in neural networks via distribution matching with the wasserstein metric")
  Member "a convex duality framework for gans":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "gans may have no nash equilibria")
  Member "adversarial distillation of bayesian neural network posteriors":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "ensemble model patching a parameter efficient variational bayesian neural network")
  Member "on wasserstein reinforcement learning and the fokker planck equation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning to score behaviors for guided policy optimization")
  Member "sbeed convergent reinforcement learning with nonlinear function approximation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "provably convergent two timescale off policy actor critic with function approximation")
  Member "adversarial variational bayes unifying variational autoencoders and generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "adversarial code learning for image generation")
  Member "boosting the actor with dual critic":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "reinforcement learning via fenchel rockafellar duality")
  Member "learning from conditional distributions via dual embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "reinforcement learning via fenchel rockafellar duality")

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)