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: "auto encoding total correlation explanation"
  Abstract: Advances in unsupervised learning enable reconstruction and generation of samples from complex distributions, but this success is marred by the inscrutability of the representations learned. We propose an information-theoretic approach to characterizing disentanglement and dependence in representation learning using multivariate mutual information, also called total correlation. The principle of total Cor-relation Ex-planation (CorEx) has motivated successful unsupervised learning applications across a variety of domains, but under some restrictive assumptions. Here we relax those restrictions by introducing a flexible variational lower bound to CorEx. Surprisingly, we find that this lower bound is equivalent to the one in variational autoencoders (VAE) under certain conditions. This information-theoretic view of VAE deepens our understanding of hierarchical VAE and motivates a new algorithm, AnchorVAE, that makes latent codes more interpretable through information maximization and enables generation of richer and more realistic samples.

Connected through hyperedge HE-1 (induced by source paper "semi supervised disentanglement with independent vector variational autoencoders"):
  with members:
    - "learning discrete and continuous factors of data via alternating disentanglement"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "isolating sources of disentanglement in variational autoencoders"
    - "disentangling latent space for vae by label relevant irrelevant dimensions"
    - "variational inference of disentangled latent concepts from unlabeled observations"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "the concrete distribution a continuous relaxation of discrete random variables"
    - "representation learning a review and new perspectives"

Connected through hyperedge HE-2 (induced by source paper "the design of global correlation quantifiers and continuous notions of statistical sufficiency"):
  with members:
    - "causal network inference by optimal causation entropy"

Connected through hyperedge HE-3 (induced by source paper "geometric disentanglement for generative latent shape models"):
  with members:
    - "shape retrieval of non rigid 3d human models"
    - "a two step disentanglement method"
    - "learning disentangled representations with reference based variational autoencoders"
    - "monte carlo convolution for learning on non uniformly sampled point clouds"
    - "a simple probabilistic deep generative model for learning generalizable disentangled representations from grouped data"
    - "hyperprior induced unsupervised disentanglement of latent representations"
    - "point convolutional neural networks by extension operators"
    - "spidercnn deep learning on point sets with parameterized convolutional filters"

Connected through hyperedge HE-4 (induced by source paper "putting an end to end to end gradient isolated learning of representations"):
  with members:
    - "data efficient image recognition with contrastive predictive coding"
    - "greedy layerwise learning can scale to imagenet"
    - "error forward propagation reusing feedforward connections to propagate errors in deep learning"
    - "biologically plausible learning algorithms can scale to large datasets"
    - "i revnet deep invertible networks"
    - "information theoretic co training"
    - "conducting credit assignment by aligning local representations"
    - "representation learning with contrastive predictive coding"

Connected through hyperedge HE-5 (induced by source paper "infogan cr disentangling generative adversarial networks with contrastive regularizers"):
  with members:
    - "infocatvae representation learning with categorical variational autoencoders"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "hyperprior induced unsupervised disentanglement of latent representations"
    - "disentangled vae representations for multi aspect and missing data"
    - "spectral normalization for generative adversarial networks"
    - "isolating sources of disentanglement in variational autoencoders"
    - "large scale gan training for high fidelity natural image synthesis"
    - "unsupervised disentangled representation learning with analogical relations"

Connected through hyperedge HE-6 (induced by source paper "riemannian normalizing flow on variational wasserstein autoencoder for text modeling"):
  with members:
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "unsupervised discrete sentence representation learning for interpretable neural dialog generation"
    - "no metrics are perfect adversarial reward learning for visual storytelling"
    - "spherical latent spaces for stable variational autoencoders"
    - "dirichlet variational autoencoder for text modeling"
    - "style transfer from non parallel text by cross alignment"
    - "a hybrid convolutional variational autoencoder for text generation"
    - "infovae information maximizing variational autoencoders"

Connected through hyperedge HE-7 (induced by source paper "exact rate distortion in autoencoders via echo noise"):
  with members:
    - "isolating sources of disentanglement in variational autoencoders"
    - "recent advances in autoencoder based representation learning"
    - "mine mutual information neural estimation"
    - "bounded information rate variational autoencoders"
    - "infovae information maximizing variational autoencoders"
    - "disentangled representations via synergy minimization"
    - "fixing a broken elbo"
    - "vae with a vampprior"

Connected through hyperedge HE-8 (induced by source paper "learning discrete and continuous factors of data via alternating disentanglement"):
  with members:
    - "efficient end to end learning for quantizable representations"
    - "isolating sources of disentanglement in variational autoencoders"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "neural discrete representation learning"
    - "the concrete distribution a continuous relaxation of discrete random variables"
    - "variational inference for monte carlo objectives"
    - "infogan interpretable representation learning by information maximizing generative adversarial nets"
    - "neural variational inference and learning in belief networks"

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)