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: "sampling generative networks"
  Abstract: We introduce several techniques for sampling and visualizing the latent spaces of generative models. Replacing linear interpolation with spherical linear interpolation prevents diverging from a model's prior distribution and produces sharper samples. J-Diagrams and MINE grids are introduced as visualizations of manifolds created by analogies and nearest neighbors. We demonstrate two new techniques for deriving attribute vectors: bias-corrected vectors with data replication and synthetic vectors with data augmentation.  Binary classification using attribute vectors is presented as a technique supporting quantitative analysis of the latent space. Most techniques are intended to be independent of model type and examples are shown on both Variational Autoencoders and Generative Adversarial Networks.

Connected through hyperedge HE-1 (induced by source paper "d vae a variational autoencoder for directed acyclic graphs"):
  with members:
    - "dag gnn dag structure learning with graph neural networks"
    - "inductive matrix completion based on graph neural networks"
    - "survey on automated machine learning"
    - "a comprehensive survey on graph neural networks"
    - "link prediction based on graph neural networks"
    - "graph convolutional policy network for goal directed molecular graph generation"
    - "constrained graph variational autoencoders for molecule design"
    - "constrained generation of semantically valid graphs via regularizing variational autoencoders"

Connected through hyperedge HE-2 (induced by source paper "diversity sensitive conditional generative adversarial networks"):
  with members:
    - "inferring semantic layout for hierarchical text to image synthesis"
    - "the gan landscape losses architectures regularization and normalization"
    - "video prediction with appearance and motion conditions"
    - "stochastic adversarial video prediction"
    - "spectral normalization for generative adversarial networks"
    - "stochastic video generation with a learned prior"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "self supervised visual planning with temporal skip connections"

Connected through hyperedge HE-3 (induced by source paper "non parametric priors for generative adversarial networks"):
  with members:
    - "disentangling 3d pose in a dendritic cnn for unconstrained 2d face alignment"
    - "exploring disentangled feature representation beyond face identification"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "progressive growing of gans for improved quality stability and variation"
    - "optimal transport maps for distribution preserving operations on latent spaces of generative models"
    - "adversarial discriminative domain adaptation"
    - "semantic interpolation in implicit models"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "improving password guessing via representation learning"):
  with members:
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "graph embedding techniques applications and performance a survey"
    - "large scale gan training for high fidelity natural image synthesis"
    - "passgan a deep learning approach for password guessing"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "learning inverse mapping by autoencoder based generative adversarial nets"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-5 (induced by source paper "on the steerability of generative adversarial networks"):
  with members:
    - "gauge equivariant convolutional networks and the icosahedral cnn"
    - "flat metric minimization with applications in generative modeling"
    - "ganalyze toward visual definitions of cognitive image properties"
    - "detecting bias with generative counterfactual face attribute augmentation"
    - "interpreting the latent space of gans for semantic face editing"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "gan dissection visualizing and understanding generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"

Connected through hyperedge HE-6 (induced by source paper "variation network learning high level attributes for controlled input manipulation"):
  with members:
    - "learning discrete and continuous factors of data via alternating disentanglement"
    - "lipschitz generative adversarial nets"
    - "diagnosing and enhancing vae models"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "deep learning based decoding for constrained sequence codes"
    - "style tokens unsupervised style modeling control and transfer in end to end speech synthesis"
    - "rednet residual encoder decoder network for indoor rgb d semantic segmentation"
    - "glsr vae geodesic latent space regularization for variational autoencoder architectures"

Connected through hyperedge HE-7 (induced by source paper "neural view interpolation for sparse light field video"):
  with members:
    - "pix2vex image to geometry reconstruction using a smooth differentiable renderer"
    - "deepview view synthesis with learned gradient descent"
    - "local light field fusion practical view synthesis with prescriptive sampling guidelines"
    - "neural volumes learning dynamic renderable volumes from images"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "stereo magnification learning view synthesis using multiplane images"
    - "learning to synthesize a 4d rgbd light field from a single image"
    - "neural audio synthesis of musical notes with wavenet autoencoders"

Connected through hyperedge HE-8 (induced by source paper "concept saliency maps to visualize relevant features in deep generative models"):
  with members:
    - "why are saliency maps noisy cause of and solution to noisy saliency maps"
    - "a theoretical explanation for perplexing behaviors of backpropagation based visualizations"
    - "sanity checks for saliency maps"
    - "smoothgrad removing noise by adding noise"
    - "adversarial variational bayes unifying variational autoencoders and generative adversarial networks"
    - "axiomatic attribution for deep networks"
    - "learning important features through propagating activation differences"
    - "the mythos of model interpretability"

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.NE(Neural and Evolutionary Computing)