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: "generative modeling of convolutional neural networks"
  Abstract: The convolutional neural networks (CNNs) have proven to be a powerful tool for discriminative learning. Recently researchers have also started to show interest in the generative aspects of CNNs in order to gain a deeper understanding of what they have learned and how to further improve them. This paper investigates generative modeling of CNNs. The main contributions include: (1) We construct a generative model for the CNN in the form of exponential tilting of a reference distribution. (2) We propose a generative gradient for pre-training CNNs by a non-parametric importance sampling scheme, which is fundamentally different from the commonly used discriminative gradient, and yet has the same computational architecture and cost as the latter. (3) We propose a generative visualization method for the CNNs by sampling from an explicit parametric image distribution. The proposed visualization method can directly draw synthetic samples for any given node in a trained CNN by the Hamiltonian Monte Carlo (HMC) algorithm, without resorting to any extra hold-out images. Experiments on the challenging ImageNet benchmark show that the proposed generative gradient pre-training consistently helps improve the performances of CNNs, and the proposed generative visualization method generates meaningful and varied samples of synthetic images from a large-scale deep CNN.

Connected through hyperedge HE-1 (induced by source paper "implicit generation and generalization in energy based models"):
  with members:
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "maximum entropy generators for energy based models"
    - "the gan landscape losses architectures regularization and normalization"
    - "learning dexterous in hand manipulation"
    - "re evaluating continual learning scenarios a categorization and case for strong baselines"
    - "learning intrinsic image decomposition from watching the world"
    - "spectral normalization for generative adversarial networks"
    - "deep anomaly detection with outlier exposure"

Connected through hyperedge HE-2 (induced by source paper "learning energy based spatial temporal generative convnets for dynamic patterns"):
  with members:
    - "videoflow a flow based generative model for video"
    - "learning descriptor networks for 3d shape synthesis and analysis"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "wasserstein introspective neural networks"
    - "two stream convolutional networks for dynamic texture synthesis"
    - "introspective classification with convolutional nets"
    - "the kinetics human action video dataset"
    - "mocogan decomposing motion and content for video generation"

Connected through hyperedge HE-3 (induced by source paper "generalizing energy based generative convnets from particle evolution perspective"):
  with members:
    - "frame revisited an interpretation view based on particle evolution"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "wasserstein introspective neural networks"
    - "density estimation using real nvp"
    - "improved techniques for training gans"
    - "deep directed generative models with energy based probability estimation"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "lsun construction of a large scale image dataset using deep learning with humans in the loop"

Connected through hyperedge HE-4 (induced by source paper "a review of learning with deep generative models from perspective of graphical modeling"):
  with members:
    - "cgans with projection discriminator"
    - "improved training of neural trans dimensional random field language models with dynamic noise contrastive estimation"
    - "the gan landscape losses architectures regularization and normalization"
    - "spectral normalization for generative adversarial networks"
    - "inference suboptimality 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"

Connected through hyperedge HE-5 (induced by source paper "interpretable convolutional neural networks via feedforward design"):
  with members:
    - "multilayer convolutional sparse modeling pursuit and dictionary learning"
    - "on data driven saak transform"
    - "explaining nonlinear classification decisions with deep taylor decomposition"
    - "theoretical insights into the optimization landscape of over parameterized shallow neural networks"
    - "interpretable convolutional neural networks"
    - "densely connected convolutional networks"
    - "understanding convolutional neural networks with a mathematical model"
    - "adversarial examples in the physical world"

Connected through hyperedge HE-6 (induced by source paper "frame revisited an interpretation view based on particle evolution"):
  with members:
    - "learning descriptor networks for 3d shape synthesis and analysis"
    - "wasserstein introspective neural networks"
    - "improved techniques for training gans"
    - "lsun construction of a large scale image dataset using deep learning with humans in the loop"
    - "rethinking the inception architecture for computer vision"
    - "learning frame models using cnn filters"
    - "very deep convolutional networks for large scale image recognition"
    - "deep learning face attributes in the wild"

Connected through hyperedge HE-7 (induced by source paper "representation learning by learning to count"):
  with members:
    - "colorization as a proxy task for visual understanding"
    - "a large contextual dataset for classification detection and counting of cars with deep learning"
    - "learning representations for automatic colorization"
    - "ambient sound provides supervision for visual learning"
    - "shuffle and learn unsupervised learning using temporal order verification"
    - "adversarial feature learning"
    - "learning features by watching objects move"
    - "unsupervised learning of visual representations by solving jigsaw puzzles"

Connected through hyperedge HE-8 (induced by source paper "fixing a broken elbo"):
  with members:
    - "block normalized gradient method an empirical study for training deep neural network"
    - "attention is all you need"
    - "pixelcnn improving the pixelcnn with discretized logistic mixture likelihood and other modifications"
    - "emergence of invariance and disentanglement in deep representations"
    - "on unifying deep generative models"
    - "improved lossy image compression with priming and spatially adaptive bit rates for recurrent networks"
    - "opening the black box of deep neural networks via information"
    - "vae with a vampprior"

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.CV(Computer Vision and Pattern Recognition)