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: "a generative process for sampling contractive auto encoders"
  Abstract: The contractive auto-encoder learns a representation of the input data that captures the local manifold structure around each data point, through the leading singular vectors of the Jacobian of the transformation from input to representation. The corresponding singular values specify how much local variation is plausible in directions associated with the corresponding singular vectors, while remaining in a high-density region of the input space. This paper proposes a procedure for generating samples that are consistent with the local structure captured by a contractive auto-encoder. The associated stochastic process defines a distribution from which one can sample, and which experimentally appears to converge quickly and mix well between modes, compared to Restricted Boltzmann Machines and Deep Belief Networks. The intuitions behind this procedure can also be used to train the second layer of contraction that pools lower-level features and learns to be invariant to the local directions of variation discovered in the first layer. We show that this can help learn and represent invariances present in the data and improve classification error.

Connected through hyperedge HE-1 (induced by source paper "from variational to deterministic autoencoders"):
  with members:
    - "diagnosing and enhancing vae models"
    - "generating diverse high fidelity images with vq vae 2"
    - "the gan landscape losses architectures regularization and normalization"
    - "introvae introspective variational autoencoders for photographic image synthesis"
    - "spectral normalization for generative adversarial networks"
    - "automatic chemical design using a data driven continuous representation of molecules"
    - "large scale gan training for high fidelity natural image synthesis"
    - "resisting adversarial attacks using gaussian mixture variational autoencoders"

Connected through hyperedge HE-2 (induced by source paper "towards robust locally linear deep networks"):
  with members:
    - "on the convergence of adam and beyond"
    - "scaling provable adversarial defenses"
    - "provable robustness of relu networks via maximization of linear regions"
    - "the cramer distance as a solution to biased wasserstein gradients"
    - "deep defense training dnns with improved adversarial robustness"
    - "on the robustness of interpretability methods"
    - "deep neural networks as 0 1 mixed integer linear programs a feasibility study"
    - "smoothgrad removing noise by adding noise"

Connected through hyperedge HE-3 (induced by source paper "density estimation and incremental learning of latent vector for generative autoencoders"):
  with members:
    - "tackling over pruning in variational autoencoders"
    - "vae with a vampprior"
    - "perceptual losses for real time style transfer and super resolution"
    - "neural autoregressive distribution estimation"
    - "density estimation using real nvp"
    - "deep feature consistent variational autoencoder"
    - "wavenet a generative model for raw audio"
    - "made masked autoencoder for distribution estimation"

Connected through hyperedge HE-4 (induced by source paper "a practical tutorial on autoencoders for nonlinear feature fusion taxonomy models software and guidelines"):
  with members:
    - "self normalizing neural networks"
    - "adversarial symmetric variational autoencoder"
    - "time series compression based on adaptive piecewise recurrent autoencoder"
    - "relational autoencoder for feature extraction"
    - "autoencoder based holographic image restoration"
    - "medical image denoising using convolutional denoising autoencoders"
    - "variational autoencoders for semi supervised text classification"
    - "generating images with perceptual similarity metrics based on deep networks"

Connected through hyperedge HE-5 (induced by source paper "geometric understanding of deep learning"):
  with members:
    - "medical image denoising using convolutional denoising autoencoders"
    - "generalized denoising auto encoders as generative models"
    - "representation learning a review and new perspectives"

Connected through hyperedge HE-6 (induced by source paper "tackling over pruning in variational autoencoders"):
  with members:
    - "improving variational inference with inverse autoregressive flow"
    - "visual dynamics probabilistic future frame synthesis via cross convolutional networks"
    - "attend infer repeat fast scene understanding with generative models"
    - "importance weighted autoencoders"
    - "attribute2image conditional image generation from visual attributes"
    - "generating sentences from a continuous space"
    - "deep convolutional inverse graphics network"
    - "generative moment matching networks"

Connected through hyperedge HE-7 (induced by source paper "deep sparse coding using optimized linear expansion of thresholds"):
  with members:
    - "maximal sparsity with deep networks"
    - "learning optimal nonlinearities for iterative thresholding algorithms"
    - "deep residual learning for image recognition"
    - "learning deep ell_0 encoders"
    - "semantic image segmentation with deep convolutional nets and fully connected crfs"
    - "overfeat integrated recognition localization and detection using convolutional networks"
    - "learning graphical model parameters with approximate marginal inference"
    - "subspace pursuit for compressive sensing signal reconstruction"

Connected through hyperedge HE-8 (induced by source paper "gsns generative stochastic networks"):
  with members:
    - "deep unsupervised learning using nonequilibrium thermodynamics"
    - "on invariance and selectivity in representation learning"
    - "deep haar scattering networks"
    - "nice non linear independent components estimation"
    - "how transferable are features in deep neural networks"
    - "reweighted wake sleep"
    - "neural variational inference and learning in belief networks"
    - "a semantic matching energy function for learning with multi relational data"

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)