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: "bit swap recursive bits back coding for lossless compression with hierarchical latent variables"
  Abstract: The bits-back argument suggests that latent variable models can be turned into lossless compression schemes. Translating the bits-back argument into efficient and practical lossless compression schemes for general latent variable models, however, is still an open problem. Bits-Back with Asymmetric Numeral Systems (BB-ANS), recently proposed by Townsend et al. (2019), makes bits-back coding practically feasible for latent variable models with one latent layer, but it is inefficient for hierarchical latent variable models. In this paper we propose Bit-Swap, a new compression scheme that generalizes BB-ANS and achieves strictly better compression rates for hierarchical latent variable models with Markov chain structure. Through experiments we verify that Bit-Swap results in lossless compression rates that are empirically superior to existing techniques. Our implementation is available at this https URL.

Connected through hyperedge HE-1 (induced by source paper "compression with flows via local bits back coding"):
  with members:
    - "practical lossless compression with latent variables using bits back coding"
    - "videoflow a flow based generative model for video"
    - "generating long sequences with sparse transformers"
    - "waveglow a flow based generative network for speech synthesis"
    - "invertible residual networks"
    - "generating high fidelity images with subscale pixel networks and multidimensional upscaling"
    - "ffjord free form continuous dynamics for scalable reversible generative models"
    - "pixelcnn improving the pixelcnn with discretized logistic mixture likelihood and other modifications"

Connected through hyperedge HE-2 (induced by source paper "integer discrete flows and lossless compression"):
  with members:
    - "discrete flows invertible generative models of discrete data"
    - "practical lossless compression with latent variables using bits back coding"
    - "videoflow a flow based generative model for video"
    - "emerging convolutions for generative normalizing flows"
    - "waveglow a flow based generative network for speech synthesis"
    - "ffjord free form continuous dynamics for scalable reversible generative models"
    - "pixelcnn improving the pixelcnn with discretized logistic mixture likelihood and other modifications"
    - "density estimation using real nvp"

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)