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: "sliced wasserstein autoencoder an embarrassingly simple generative model"
  Abstract: In this paper we study generative modeling via autoencoders while using the elegant geometric properties of the optimal transport (OT) problem and the Wasserstein distances. We introduce Sliced-Wasserstein Autoencoders (SWAE), which are generative models that enable one to shape the distribution of the latent space into any samplable probability distribution without the need for training an adversarial network or defining a closed-form for the distribution. In short, we regularize the autoencoder loss with the sliced-Wasserstein distance between the distribution of the encoded training samples and a predefined samplable distribution. We show that the proposed formulation has an efficient numerical solution that provides similar capabilities to Wasserstein Autoencoders (WAE) and Variational Autoencoders (VAE), while benefiting from an embarrassingly simple implementation.

Connected through hyperedge HE-1 (induced by source paper "learning implicit generative models with theoretical guarantees"):
  with members:
    - "deep generative learning via variational gradient flow"
    - "sinkhorn autoencoders"
    - "wasserstein wasserstein auto encoders"
    - "spectral normalization for generative adversarial networks"
    - "monge amp ere flow for generative modeling"
    - "ffjord free form continuous dynamics for scalable reversible generative models"
    - "generative modeling using the sliced wasserstein distance"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"

Connected through hyperedge HE-2 (induced by source paper "learning autoencoders with relational regularization"):
  with members:
    - "hierarchical optimal transport for document representation"
    - "scalable gromov wasserstein learning for graph partitioning and matching"
    - "learning generative models across incomparable spaces"
    - "gromov wasserstein factorization models for graph clustering"
    - "gromov wasserstein alignment of word embedding spaces"
    - "the gromov wasserstein distance between networks and stable network invariants"
    - "deep unsupervised clustering with gaussian mixture variational autoencoders"
    - "multi view generative adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "max sliced wasserstein distance and its use for gans"):
  with members:
    - "sliced wasserstein generative models"
    - "multimodal unsupervised image to image translation"
    - "improving gans using optimal transport"
    - "generative modeling using the sliced wasserstein distance"
    - "xgan unsupervised image to image translation for many to many mappings"
    - "which training methods for gans do actually converge"
    - "mcgan mean and covariance feature matching gan"
    - "began boundary equilibrium generative adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "one element batch training by moving window"):
  with members:
    - "sliced generative models"
    - "generative adversarial networks recent developments"
    - "towards photographic image manipulation with balanced growing of generative autoencoders"
    - "pioneer networks progressively growing generative autoencoder"
    - "cramer wold autoencoder"
    - "set aggregation network for structured data processing"
    - "revisiting small batch training for deep neural networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"

Connected through hyperedge HE-5 (induced by source paper "sinkhorn autoencoders"):
  with members:
    - "implicit reparameterization gradients"
    - "deeppermnet visual permutation learning"
    - "lipschitz properties for deep convolutional networks"
    - "fixing a broken elbo"
    - "representation learning by learning to count"
    - "vae with a vampprior"
    - "deep variational information bottleneck"
    - "tsallis regularized optimal transport and ecological inference"

Connected through hyperedge HE-6 (induced by source paper "wasserstein neural processes"):
  with members:
    - "generalized sliced wasserstein distances"
    - "attentive neural processes"
    - "conditional neural processes"
    - "improved training of wasserstein gans"
    - "sliced wasserstein kernels for probability distributions"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-7 (induced by source paper "subspace robust wasserstein distances"):
  with members:
    - "generative modeling using the sliced wasserstein distance"
    - "approximating the quadratic transportation metric in near linear time"
    - "advances in pre training distributed word representations"
    - "sliced wasserstein kernels for probability distributions"
    - "learning probability measures with respect to optimal transport metrics"
    - "explicit approximations of the gaussian kernel"

Connected through hyperedge HE-8 (induced by source paper "training generative reversible networks"):
  with members:
    - "improving gans using optimal transport"
    - "i revnet deep invertible networks"
    - "the cramer distance as a solution to biased wasserstein gradients"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "it takes only two adversarial generator encoder networks"
    - "the reversible residual network backpropagation without storing activations"
    - "progressive growing of gans for improved quality stability and variation"
    - "comparison of maximum likelihood and gan based training of real nvps"

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)