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: "hidden talents of the variational autoencoder"
  Abstract: Variational autoencoders (VAE) represent a popular, flexible form of deep generative model that can be stochastically fit to samples from a given random process using an information-theoretic variational bound on the true underlying distribution. Once so-obtained, the model can be putatively used to generate new samples from this distribution, or to provide a low-dimensional latent representation of existing samples. While quite effective in numerous application domains, certain important mechanisms which govern the behavior of the VAE are obfuscated by the intractable integrals and resulting stochastic approximations involved. Moreover, as a highly non-convex model, it remains unclear exactly how minima of the underlying energy relate to original design purposes. We attempt to better quantify these issues by analyzing a series of tractable special cases of increasing complexity. In doing so, we unveil interesting connections with more traditional dimensionality reduction models, as well as an intrinsic yet underappreciated propensity for robustly dismissing sparse outliers when estimating latent manifolds. With respect to the latter, we demonstrate that the VAE can be viewed as the natural evolution of recent robust PCA models, capable of learning nonlinear manifolds of unknown dimension obscured by gross corruptions.

Connected through hyperedge HE-1 (induced by source paper "the usual suspects reassessing blame for vae posterior collapse"):
  with members:
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "diagnosing and enhancing vae models"
    - "small nonlinearities in activation functions create bad local minima in neural networks"
    - "variational autoencoders pursue pca directions by accident"
    - "improving explorability in variational inference with annealed variational objectives"
    - "tackling over pruning in variational autoencoders"
    - "fixing a broken elbo"
    - "vae with a vampprior"

Connected through hyperedge HE-2 (induced by source paper "don t blame the elbo a linear vae perspective on posterior collapse"):
  with members:
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "diagnosing and enhancing vae models"
    - "preventing posterior collapse with delta vaes"
    - "loss landscapes of regularized linear autoencoders"
    - "automatic chemical design using a data driven continuous representation of molecules"
    - "isolating sources of disentanglement in variational autoencoders"
    - "inference suboptimality in variational autoencoders"
    - "variational autoencoders pursue pca directions by accident"

Connected through hyperedge HE-3 (induced by source paper "variational autoencoders pursue pca directions by accident"):
  with members:
    - "diagnosing and enhancing vae models"
    - "from variational to deterministic autoencoders"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "visual reinforcement learning with imagined goals"
    - "isolating sources of disentanglement in variational autoencoders"
    - "adversarial variational bayes unifying variational autoencoders and generative adversarial networks"
    - "advances in variational inference"
    - "improved variational autoencoders for text modeling using dilated convolutions"

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)