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 "augmented normalizing flows bridging the gap between generative flows and latent variable models"):
  Abstract: In this work, we propose a new family of generative flows on an augmented data space, with an aim to improve expressivity without drastically increasing the computational cost of sampling and evaluation of a lower bound on the likelihood. Theoretically, we prove the proposed flow can approximate a Hamiltonian ODE as a universal transport map. Empirically, we demonstrate state-of-the-art performance on standard benchmarks of flow-based generative modeling.

Includes members:
  Member "set flow a permutation invariant normalizing flow":
    also connected through other hyperedges:
      (none)
  Member "mintnet building invertible neural networks with masked convolutions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning discrete distributions by dequantization")
  Member "hybrid models with deep and invertible features":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "exact information bottleneck with invertible neural networks getting the best of discriminative and generative modeling")
  Member "emerging convolutions for generative normalizing flows":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "relational state space model for stochastic multi object systems")
  Member "improving exploration in soft actor critic with normalizing flows policies":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "discrete and continuous action representation for practical rl in video games")
  Member "leveraging exploration in off policy algorithms via normalizing flows":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "provably efficient reconstruction of policy networks")
  Member "stochastic neural network with kronecker flow":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "dissecting non vacuous generalization bounds based on the mean field approximation")
  Member "invertible residual networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on feature normalization and data augmentation")

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)