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: "deep unsupervised learning using nonequilibrium thermodynamics"
  Abstract: A central problem in machine learning involves modeling complex data-sets using highly flexible families of probability distributions in which learning, sampling, inference, and evaluation are still analytically or computationally tractable. Here, we develop an approach that simultaneously achieves both flexibility and tractability. The essential idea, inspired by non-equilibrium statistical physics, is to systematically and slowly destroy structure in a data distribution through an iterative forward diffusion process. We then learn a reverse diffusion process that restores structure in data, yielding a highly flexible and tractable generative model of the data. This approach allows us to rapidly learn, sample from, and evaluate probabilities in deep generative models with thousands of layers or time steps, as well as to compute conditional and posterior probabilities under the learned model. We additionally release an open source reference implementation of the algorithm.

Connected through hyperedge HE-1 (induced by source paper "training deep energy based models with f divergence minimization"):
  with members:
    - "exponential family estimation via adversarial dynamics embedding"
    - "maximum entropy generators for energy based models"
    - "kernel exponential family estimation via doubly dual embedding"
    - "spectral normalization for generative adversarial networks"
    - "which training methods for gans do actually converge"
    - "improved training of wasserstein gans"
    - "gradient descent gan optimization is locally stable"
    - "seqgan sequence generative adversarial nets with policy gradient"

Connected through hyperedge HE-2 (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-3 (induced by source paper "generative modeling by estimating gradients of the data distribution"):
  with members:
    - "implicit generation and generalization in energy based models"
    - "sliced score matching a scalable approach to density and score estimation"
    - "learning implicit generative models with the method of learned moments"
    - "spectral normalization for generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "autoregressive quantile networks for generative modeling"
    - "a style based generator architecture for generative adversarial networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"

Connected through hyperedge HE-4 (induced by source paper "regularising deep networks with deep generative models"):
  with members:
    - "data augmentation using gans"
    - "gan augmentation augmenting training data using generative adversarial networks"
    - "gain missing data imputation using generative adversarial nets"
    - "handling incomplete heterogeneous data using vaes"
    - "gan based synthetic medical image augmentation for increased cnn performance in liver lesion classification"
    - "low shot learning from imaginary data"
    - "on calibration of modern neural networks"
    - "the effectiveness of data augmentation in image classification using deep learning"

Connected through hyperedge HE-5 (induced by source paper "variational auto decoder"):
  with members:
    - "gain missing data imputation using generative adversarial nets"
    - "autoencoders and probabilistic inference with missing data an exact solution for the factor analysis case"
    - "handling incomplete heterogeneous data using vaes"
    - "high resolution image inpainting using multi scale neural patch synthesis"
    - "improved techniques for training gans"
    - "context encoders feature learning by inpainting"
    - "adam a method for stochastic optimization"
    - "efficient em training of gaussian mixtures with missing data"

Connected through hyperedge HE-6 (induced by source paper "neural joint source channel coding"):
  with members:
    - "deepcode feedback codes via deep learning"
    - "deep joint source channel coding for wireless image transmission"
    - "discrete autoencoders for sequence models"
    - "theory and experiments on vector quantized autoencoders"
    - "deep learning for joint source channel coding of text"
    - "backpropagation through the void optimizing control variates for black box gradient estimation"
    - "rebar low variance unbiased gradient estimates for discrete latent variable models"
    - "infovae information maximizing variational autoencoders"

Connected through hyperedge HE-7 (induced by source paper "mixed batches and symmetric discriminators for gan training"):
  with members:
    - "spectral normalization for generative adversarial networks"
    - "deep sets"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "progressive growing of gans for improved quality stability and variation"
    - "attention is all you need"
    - "density estimation using real nvp"
    - "permutation equivariant neural networks applied to dynamics prediction"
    - "improved techniques for training gans"

Connected through hyperedge HE-8 (induced by source paper "generative adversarial networks with decoder encoder output noise"):
  with members:
    - "adversarial variational bayes unifying variational autoencoders and generative adversarial networks"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "conditional image generation with pixelcnn decoders"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "draw a recurrent neural network for image generation"
    - "from facial parts responses to face detection a deep learning approach"
    - "autoencoding beyond pixels using a learned similarity metric"

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)