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 "compression with flows via local bits back coding"):
  Abstract: Likelihood-based generative models are the backbones of lossless compression, due to the guaranteed existence of codes with lengths close to negative log likelihood. However, there is no guaranteed existence of computationally efficient codes that achieve these lengths, and coding algorithms must be hand-tailored to specific types of generative models to ensure computational efficiency. Such coding algorithms are known for autoregressive models and variational autoencoders, but not for general types of flow models. To fill in this gap, we introduce local bits-back coding, a new compression technique compatible with flow models. We present efficient algorithms that instantiate our technique for many popular types of flows, and we demonstrate that our algorithms closely achieve theoretical codelengths for state-of-the-art flow models on high-dimensional data.

Includes members:
  Member "practical lossless compression with latent variables using bits back coding":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a tutorial on the range variant of asymmetric numeral systems")
  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 "generating long sequences with sparse transformers":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "meta embeddings based on self attention")
  Member "bit swap recursive bits back coding for lossless compression with hierarchical latent variables":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "integer discrete flows and lossless compression")
  Member "waveglow a flow based generative network for speech synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "videoflow a conditional flow based model for stochastic video generation")
  Member "invertible residual networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on feature normalization and data augmentation")
  Member "generating high fidelity images with subscale pixel networks and multidimensional upscaling":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "blur noise and compression robust generative adversarial networks")
  Member "ffjord free form continuous dynamics for scalable reversible generative models":
    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)