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: "mae mutual posterior divergence regularization for variational autoencoders"
  Abstract: Variational Autoencoder (VAE), a simple and effective deep generative model, has led to a number of impressive empirical successes and spawned many advanced variants and theoretical investigations. However, recent studies demonstrate that, when equipped with expressive generative distributions (aka. decoders), VAE suffers from learning uninformative latent representations with the observation called KL Varnishing, in which case VAE collapses into an unconditional generative model. In this work, we introduce mutual posterior-divergence regularization, a novel regularization that is able to control the geometry of the latent space to accomplish meaningful representation learning, while achieving comparable or superior capability of density estimation. Experiments on three image benchmark datasets demonstrate that, when equipped with powerful decoders, our model performs well both on density estimation and representation learning.

Connected through hyperedge HE-1 (induced by source paper "flowseq non autoregressive conditional sequence generation with generative flow"):
  with members:
    - "non autoregressive machine translation with auxiliary regularization"
    - "macow masked convolutional generative flow"
    - "insertion based decoding with automatically inferred generation order"
    - "insertion transformer flexible sequence generation via insertion operations"
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "latent variable non autoregressive neural machine translation with deterministic inference using a delta posterior"
    - "mask predict parallel decoding of conditional masked language models"
    - "mixture models for diverse machine translation tricks of the trade"

Connected through hyperedge HE-2 (induced by source paper "macow masked convolutional generative flow"):
  with members:
    - "emerging convolutions for generative normalizing flows"
    - "waveglow a flow based generative network for speech synthesis"
    - "generating high fidelity images with subscale pixel networks and multidimensional upscaling"
    - "progressive growing of gans for improved quality stability and variation"
    - "pixelsnail an improved autoregressive generative model"
    - "density estimation using real nvp"
    - "conditional image generation with pixelcnn decoders"
    - "wavenet a generative model for raw audio"

Connected through hyperedge HE-3 (induced by source paper "noise contrastive variational autoencoders"):
  with members:
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "contrastive variational autoencoder enhances salient features"
    - "preventing posterior collapse with delta vaes"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "bounded information rate variational autoencoders"
    - "infovae information maximizing variational autoencoders"
    - "improved variational autoencoders for text modeling using dilated convolutions"
    - "fixing a broken elbo"

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)