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 "dimensionality reduction flows"):
  Abstract: Deep generative modelling using flows has gained popularity owing to the tractable exact log-likelihood estimation with efficient training and synthesis process. Trained flow models carry rich information about the structure and local variance in input data. However, a bottleneck for flow models to scale with increasing dimensions is that the latent space has same size as the high-dimensional input space. In this paper, we propose methods to reduce the latent space dimension of flow models. Our first approach includes replacing standard high dimensional prior with a learned prior from a low dimensional noise space. Further improving to achieve exact log-likelihood with reduced dimensionality, our second approach presents an improved multi-scale architecture (Dinh et al., 2016) via likelihood contribution based factorization of dimensions. Using our method over state-of-the-art flow models, we demonstrate improvements in log-likelihood score on standard image benchmarks. Our work ventures a data dependent factorization scheme which is more efficient than static counterparts in prior works.

Includes members:
  Member "flow improving flow based generative models with variational dequantization and architecture design":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "woodbury transformations for deep generative flows")
  Member "videoflow a flow based generative model for video":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi variate probabilistic time series forecasting via conditioned normalizing flows")
  Member "semi supervised learning with normalizing flows":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "understanding the limitations of conditional generative models")
  Member "emerging convolutions for generative normalizing flows":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "relational state space model for stochastic multi object systems")
  Member "gansynth adversarial neural audio synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on leveraging pretrained gans for limited data generation")
  Member "a style based generator architecture for generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "pixelcnn improving the pixelcnn with discretized logistic mixture likelihood and other modifications":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "adaptive density estimation for generative models")
  Member "pixelsnail an improved autoregressive generative model":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "augmented normalizing flows bridging the gap between generative flows and latent variable models")

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)