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: "the riemannian geometry of deep generative models"
  Abstract: Deep generative models learn a mapping from a low dimensional latent space to a high-dimensional data space. Under certain regularity conditions, these models parameterize nonlinear manifolds in the data space. In this paper, we investigate the Riemannian geometry of these generated manifolds. First, we develop efficient algorithms for computing geodesic curves, which provide an intrinsic notion of distance between points on the manifold. Second, we develop an algorithm for parallel translation of a tangent vector along a path on the manifold. We show how parallel translation can be used to generate analogies, i.e., to transport a change in one data point into a semantically similar change of another data point. Our experiments on real image data show that the manifolds learned by deep generative models, while nonlinear, are surprisingly close to zero curvature. The practical implication is that linear paths in the latent space closely approximate geodesics on the generated manifold. However, further investigation into this phenomenon is warranted, to identify if there are other architectures or datasets where curvature plays a more prominent role. We believe that exploring the Riemannian geometry of deep generative models, using the tools developed in this paper, will be an important step in understanding the high-dimensional, nonlinear spaces these models learn.

Connected through hyperedge HE-1 (induced by source paper "on implicit regularization in beta vaes"):
  with members:
    - "are disentangled representations helpful for abstract visual reasoning"
    - "from variational to deterministic autoencoders"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "isolating sources of disentanglement in variational autoencoders"
    - "latent space non linear statistics"
    - "explaining explanations an overview of interpretability of machine learning"
    - "variational autoencoders pursue pca directions by accident"
    - "variational inference of disentangled latent concepts from unlabeled observations"

Connected through hyperedge HE-2 (induced by source paper "disentangling representations using gaussian processes in variational autoencoders for video prediction"):
  with members:
    - "bayes factor vae hierarchical bayesian deep auto encoder models for factor disentanglement"
    - "geometry of deep generative models for disentangled representations"
    - "isolating sources of disentanglement in variational autoencoders"
    - "latent space non linear statistics"
    - "disentangled sequential autoencoder"
    - "gaussian process prior variational autoencoders"
    - "learning to decompose and disentangle representations for video prediction"
    - "decomposing motion and content for natural video sequence prediction"

Connected through hyperedge HE-3 (induced by source paper "rate distortion optimization guided autoencoder for isometric embedding in euclidean latent space"):
  with members:
    - "exact rate distortion in autoencoders via echo noise"
    - "adversarially learned anomaly detection"
    - "recent advances in autoencoder based representation learning"
    - "variational autoencoders pursue pca directions by accident"
    - "fixing a broken elbo"
    - "adam a method for stochastic optimization"
    - "nice non linear independent components estimation"
    - "deep learning face attributes in the wild"

Connected through hyperedge HE-4 (induced by source paper "interpreting the latent space of gans for semantic face editing"):
  with members:
    - "lia latently invertible autoencoder with adversarial learning"
    - "on the steerability of generative adversarial networks"
    - "ganalyze toward visual definitions of cognitive image properties"
    - "facefeat gan a two stage approach for identity preserving face synthesis"
    - "spectral normalization for generative adversarial networks"
    - "towards open set identity preserving face synthesis"
    - "latent space non linear statistics"
    - "elegant exchanging latent encodings with gan for transferring multiple face attributes"

Connected through hyperedge HE-5 (induced by source paper "product of orthogonal spheres parameterization for disentangled representation learning"):
  with members:
    - "emerging disentanglement in auto encoder based unsupervised image content transfer"
    - "learning disentangled representations with reference based variational autoencoders"
    - "disentangling pose from appearance in monochrome hand images"
    - "geometry of deep generative models for disentangled representations"
    - "disentangled representation learning for non parallel text style transfer"
    - "mr gan manifold regularized generative adversarial networks"
    - "isolating sources of disentanglement in variational autoencoders"
    - "disentangling factors of variation with cycle consistent variational auto encoders"

Connected through hyperedge HE-6 (induced by source paper "dimensionality compression and expansion in deep neural networks"):
  with members:
    - "geometry of deep generative models for disentangled representations"
    - "the local dimension of deep manifold"
    - "energy entropy competition and the effectiveness of stochastic gradient descent in machine learning"
    - "understanding convolutional neural networks with information theory an initial exploration"
    - "fisher rao metric geometry and complexity of neural networks"
    - "towards understanding generalization of deep learning perspective of loss landscapes"
    - "a bayesian perspective on generalization and stochastic gradient descent"
    - "understanding deep learning requires rethinking generalization"

Connected through hyperedge HE-7 (induced by source paper "deepflow history matching in the space of deep generative models"):
  with members:
    - "cyclical stochastic gradient mcmc for bayesian deep learning"
    - "cgans with projection discriminator"
    - "analyzing inverse problems with invertible neural networks"
    - "probabilistic semantic inpainting with pixel constrained cnns"
    - "adversarial regularizers in inverse problems"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "sampling generative networks"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-8 (induced by source paper "adversarial autoencoders with constant curvature latent manifolds"):
  with members:
    - "hyperbolic attention networks"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "hyperbolic neural networks"
    - "automatic chemical design using a data driven continuous representation of molecules"
    - "spherical latent spaces for stable variational autoencoders"
    - "anomaly and change detection in graph streams through constant curvature manifold embeddings"
    - "dynamic edge conditioned filters in convolutional neural networks on graphs"
    - "geometric deep learning going beyond euclidean 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)