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: "neural importance sampling"
  Abstract: We propose to use deep neural networks for generating samples in Monte Carlo integration. Our work is based on non-linear independent components estimation (NICE), which we extend in numerous ways to improve performance and enable its application to integration problems. First, we introduce piecewise-polynomial coupling transforms that greatly increase the modeling power of individual coupling layers. Second, we propose to preprocess the inputs of neural networks using one-blob encoding, which stimulates localization of computation and improves inference. Third, we derive a gradient-descent-based optimization for the KL and the $\chi^2$ divergence for the specific application of Monte Carlo integration with unnormalized stochastic estimates of the target distribution. Our approach enables fast and accurate inference and efficient sample generation independently of the dimensionality of the integration domain. We show its benefits on generating natural images and in two applications to light-transport simulation: first, we demonstrate learning of joint path-sampling densities in the primary sample space and importance sampling of multi-dimensional path prefixes thereof. Second, we use our technique to extract conditional directional densities driven by the product of incident illumination and the BSDF in the rendering equation, and we leverage the densities for path guiding. In all applications, our approach yields on-par or higher performance than competing techniques at equal sample count.

Connected through hyperedge HE-1 (induced by source paper "flow improving flow based generative models with variational dequantization and architecture design"):
  with members:
    - "efficient neural audio synthesis"
    - "fast generation for convolutional autoregressive models"
    - "progressive growing of gans for improved quality stability and variation"
    - "attention is all you need"
    - "pixelcnn improving the pixelcnn with discretized logistic mixture likelihood and other modifications"
    - "a simple neural attentive meta learner"
    - "pixelsnail an improved autoregressive generative model"
    - "improving variational inference with inverse autoregressive flow"

Connected through hyperedge HE-2 (induced by source paper "stochastic neural network with kronecker flow"):
  with members:
    - "functional variational bayesian neural networks"
    - "pac bayes with backprop"
    - "randomized value functions via multiplicative normalizing flows"
    - "invertible residual networks"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "efficient exploration through bayesian deep q networks"
    - "exploring generalization in deep learning"
    - "noisy natural gradient as variational inference"

Connected through hyperedge HE-3 (induced by source paper "hierarchical importance weighted autoencoders"):
  with members:
    - "importance weighting and variational inference"
    - "differentiable antithetic sampling for variance reduction in stochastic variational inference"
    - "inference suboptimality in variational autoencoders"
    - "doubly reparameterized gradient estimators for monte carlo objectives"
    - "filtering variational objectives"
    - "variational inference for monte carlo objectives"
    - "importance weighted autoencoders"
    - "fast and accurate deep network learning by exponential linear units elus"

Connected through hyperedge HE-4 (induced by source paper "identifying through flows for recovering latent representations"):
  with members:
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "isolating sources of disentanglement in variational autoencoders"
    - "variational inference of disentangled latent concepts from unlabeled observations"
    - "density estimation using real nvp"
    - "nice non linear independent components estimation"
    - "representation learning a review and new perspectives"

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)