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: "improving variational auto encoders using householder flow"
  Abstract: Variational auto-encoders (VAE) are scalable and powerful generative models. However, the choice of the variational posterior determines tractability and flexibility of the VAE. Commonly, latent variables are modeled using the normal distribution with a diagonal covariance matrix. This results in computational efficiency but typically it is not flexible enough to match the true posterior distribution. One fashion of enriching the variational posterior distribution is application of normalizing flows, i.e., a series of invertible transformations to latent variables with a simple posterior. In this paper, we follow this line of thinking and propose a volume-preserving flow that uses a series of Householder transformations. We show empirically on MNIST dataset and histopathology data that the proposed flow allows to obtain more flexible variational posterior and competitive results comparing to other normalizing flows.

Connected through hyperedge HE-1 (induced by source paper "augmented normalizing flows bridging the gap between generative flows and latent variable models"):
  with members:
    - "set flow a permutation invariant normalizing flow"
    - "mintnet building invertible neural networks with masked convolutions"
    - "hybrid models with deep and invertible features"
    - "emerging convolutions for generative normalizing flows"
    - "improving exploration in soft actor critic with normalizing flows policies"
    - "leveraging exploration in off policy algorithms via normalizing flows"
    - "stochastic neural network with kronecker flow"
    - "invertible residual networks"

Connected through hyperedge HE-2 (induced by source paper "gradient boosted flows"):
  with members:
    - "inference suboptimality in variational autoencoders"
    - "ffjord free form continuous dynamics for scalable reversible generative models"
    - "gaussian process prior variational autoencoders"
    - "improving explorability in variational inference with annealed variational objectives"
    - "vae with a vampprior"
    - "improving variational inference with inverse autoregressive flow"
    - "density estimation using real nvp"
    - "variational lossy autoencoder"

Connected through hyperedge HE-3 (induced by source paper "variational item response theory fast accurate and expressive"):
  with members:
    - "the stanford acuity test a probabilistic approach for precise visual acuity testing"
    - "deep irt make deep learning based knowledge tracing explainable using item response theory"
    - "neural autoregressive flows"
    - "pyro deep universal probabilistic programming"
    - "multimodal generative models for scalable weakly supervised learning"
    - "attention is all you need"
    - "dynamic key value memory networks for knowledge tracing"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "gaussianization flows"):
  with members:
    - "ffjord free form continuous dynamics for scalable reversible generative models"
    - "density estimation using real nvp"
    - "made masked autoencoder for distribution estimation"
    - "nice non linear independent components estimation"

Connected through hyperedge HE-5 (induced by source paper "an introduction to variational autoencoders"):
  with members:
    - "automatic chemical design using a data driven continuous representation of molecules"
    - "isolating sources of disentanglement in variational autoencoders"
    - "a hybrid convolutional variational autoencoder for text generation"
    - "improved variational autoencoders for text modeling using dilated convolutions"
    - "bayesian recurrent neural networks"
    - "flow gan combining maximum likelihood and adversarial learning in generative models"
    - "learning discourse level diversity for neural dialog models using conditional variational autoencoders"
    - "voice conversion from unaligned corpora using variational autoencoding wasserstein generative adversarial networks"

Connected through hyperedge HE-6 (induced by source paper "multimodal generative models for compositional representation learning"):
  with members:
    - "factorized inference in deep markov models for incomplete multimodal time series"
    - "measuring compositionality in representation learning"
    - "vae gans for probabilistic compressive image recovery uncertainty analysis"
    - "shapeglot learning language for shape differentiation"
    - "neural autoregressive flows"
    - "graph r cnn for scene graph generation"
    - "yolov3 an incremental improvement"
    - "multimodal matching using a hybrid convolutional neural network"

Connected through hyperedge HE-7 (induced by source paper "topic guided variational autoencoders for text generation"):
  with members:
    - "sequence generation with guider network"
    - "a reinforced topic aware convolutional sequence to sequence model for abstractive text summarization"
    - "nash toward end to end neural architecture for generative semantic hashing"
    - "distilled wasserstein learning for word embedding and topic modeling"
    - "texygen a benchmarking platform for text generation models"
    - "toward controlled generation of text"
    - "a hybrid convolutional variational autoencoder for text generation"
    - "selective encoding for abstractive sentence summarization"

Connected through hyperedge HE-8 (induced by source paper "conditional flow variational autoencoders for structured sequence prediction"):
  with members:
    - "graph neural networks for modelling traffic participant interaction"
    - "scene induced multi modal trajectory forecasting via planning"
    - "riemannian normalizing flow on variational wasserstein autoencoder for text modeling"
    - "multi agent tensor fusion for contextual trajectory prediction"
    - "preventing posterior collapse with delta vaes"
    - "structured output learning with conditional generative flows"
    - "cyclical annealing schedule a simple approach to mitigating kl vanishing"
    - "videoflow a flow based generative model for video"

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)