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: "denoising criterion for variational auto encoding framework"
  Abstract: Denoising autoencoders (DAE) are trained to reconstruct their clean inputs with noise injected at the input level, while variational autoencoders (VAE) are trained with noise injected in their stochastic hidden layer, with a regularizer that encourages this noise injection. In this paper, we show that injecting noise both in input and in the stochastic hidden layer can be advantageous and we propose a modified variational lower bound as an improved objective function in this setup. When input is corrupted, then the standard VAE lower bound involves marginalizing the encoder conditional distribution over the input noise, which makes the training criterion intractable. Instead, we propose a modified training criterion which corresponds to a tractable bound when input is corrupted. Experimentally, we find that the proposed denoising variational autoencoder (DVAE) yields better average log-likelihood than the VAE and the importance weighted autoencoder on the MNIST and Frey Face datasets.

Connected through hyperedge HE-1 (induced by source paper "recvae a new variational autoencoder for top n recommendations with implicit feedback"):
  with members:
    - "embarrassingly shallow autoencoders for sparse data"
    - "towards amortized ranking critical training for collaborative filtering"
    - "on the necessity and effectiveness of learning the prior of variational auto encoder"
    - "isolating sources of disentanglement in variational autoencoders"
    - "boosting trust region policy optimization by normalizing flows policy"
    - "searching for activation functions"
    - "vae with a vampprior"
    - "proximal policy optimization algorithms"

Connected through hyperedge HE-2 (induced by source paper "information bottleneck and its applications in deep learning"):
  with members:
    - "a direct sum result for the information complexity of learning"
    - "chaining mutual information and tightening generalization bounds"
    - "mathematics of deep learning"
    - "infovae information maximizing variational autoencoders"
    - "emergence of invariance and disentanglement in deep representations"
    - "stochastic gradient descent performs variational inference converges to limit cycles for deep networks"
    - "fixing a broken elbo"
    - "sharp minima can generalize for deep nets"

Connected through hyperedge HE-3 (induced by source paper "educating text autoencoders latent representation guidance via denoising"):
  with members:
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "universal transformers"
    - "eval all trust a few do wrong to none comparing sentence generation models"
    - "multiple attribute text style transfer"
    - "content preserving text generation with attribute controls"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "phrase based neural unsupervised machine translation"
    - "toward controlled generation of text"

Connected through hyperedge HE-4 (induced by source paper "quantization based regularization for autoencoders"):
  with members:
    - "preventing posterior collapse with delta vaes"
    - "understanding and improving interpolation in autoencoders via an adversarial regularizer"
    - "clustering with deep learning taxonomy and new methods"
    - "fast decoding in sequence models using discrete latent variables"
    - "som vae interpretable discrete representation learning on time series"
    - "information theoretic analysis of generalization capability of learning algorithms"
    - "advances in variational inference"
    - "soft to hard vector quantization for end to end learning compressible representations"

Connected through hyperedge HE-5 (induced by source paper "learning 3d shape completion under weak supervision"):
  with members:
    - "multi view consistency as supervisory signal for learning shape and pose prediction"
    - "learning efficient point cloud generation for dense 3d object reconstruction"
    - "octnetfusion learning depth fusion from data"
    - "high resolution shape completion using deep neural networks for global structure and local geometry inference"
    - "octree generating networks efficient convolutional architectures for high resolution 3d outputs"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "hierarchical surface prediction for 3d object reconstruction"
    - "shape inpainting using 3d generative adversarial network and recurrent convolutional networks"

Connected through hyperedge HE-6 (induced by source paper "quantitatively evaluating gans with divergences proposed for training"):
  with members:
    - "on convergence and stability of gans"
    - "the cramer distance as a solution to biased wasserstein gradients"
    - "began boundary equilibrium generative adversarial networks"
    - "generative adversarial parallelization"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "comparison of maximum likelihood and gan based training of real nvps"
    - "the numerics of gans"
    - "mmd gan towards deeper understanding of moment matching network"

Connected through hyperedge HE-7 (induced by source paper "stochastic variational inference with gradient linearization"):
  with members:
    - "probflow joint optical flow and uncertainty estimation"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "variational inference for monte carlo objectives"
    - "flow fields dense correspondence fields for highly accurate large displacement optical flow estimation"
    - "the fast bilateral solver"
    - "epicflow edge preserving interpolation of correspondences for optical flow"
    - "adam a method for stochastic optimization"
    - "efficient inference in fully connected crfs with gaussian edge potentials"

Connected through hyperedge HE-8 (induced by source paper "denoising adversarial autoencoders classifying skin lesions using limited labelled training data"):
  with members:
    - "skin lesion analysis towards melanoma detection using deep learning network"
    - "automatic skin lesion analysis using large scale dermoscopy images and deep residual networks"
    - "skin lesion analysis toward melanoma detection a challenge at the 2017 international symposium on biomedical imaging isbi hosted by the international skin imaging collaboration isic"
    - "unsupervised representation learning with deep convolutional generative adversarial 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)