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: "inverting the generator of a generative adversarial network ii"
  Abstract: Generative adversarial networks (GANs) learn a deep generative model that is able to synthesise novel, high-dimensional data samples. New data samples are synthesised by passing latent samples, drawn from a chosen prior distribution, through the generative model. Once trained, the latent space exhibits interesting properties, that may be useful for down stream tasks such as classification or retrieval. Unfortunately, GANs do not offer an "inverse model", a mapping from data space back to latent space, making it difficult to infer a latent representation for a given data sample. In this paper, we introduce a technique, inversion, to project data samples, specifically images, to the latent space using a pre-trained GAN. Using our proposed inversion technique, we are able to identify which attributes of a dataset a trained GAN is able to model and quantify GAN performance, based on a reconstruction loss. We demonstrate how our proposed inversion technique may be used to quantitatively compare performance of various GAN models trained on three image datasets. We provide code for all of our experiments, this https URL

Connected through hyperedge HE-1 (induced by source paper "controlling generative models with continuous factors of variations"):
  with members:
    - "generating diverse high fidelity images with vq vae 2"
    - "on the steerability of generative adversarial networks"
    - "ganalyze toward visual definitions of cognitive image properties"
    - "deeply supervised salient object detection with short connections"
    - "gan augmentation augmenting training data using generative adversarial networks"
    - "gan dissection visualizing and understanding generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "the unreasonable effectiveness of deep features as a perceptual metric"

Connected through hyperedge HE-2 (induced by source paper "meshgan non linear 3d morphable models of faces"):
  with members:
    - "generating 3d faces using convolutional mesh autoencoders"
    - "relational inductive biases deep learning and graph networks"
    - "escaping from collapsing modes in a constrained space"
    - "a simple approach to intrinsic correspondence learning on unstructured 3d meshes"
    - "motifnet a motif based graph convolutional network for directed graphs"
    - "nonlinear 3d face morphable model"
    - "variational autoencoders for deforming 3d mesh models"
    - "began boundary equilibrium generative adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "out domain examples for generative models"):
  with members:
    - "the gan landscape losses architectures regularization and normalization"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "black box adversarial attacks with limited queries and information"
    - "adversarial attacks on neural network policies"
    - "progressive growing of gans for improved quality stability and variation"
    - "precise recovery of latent vectors from generative adversarial networks"
    - "adversarial examples for semantic segmentation and object detection"
    - "adversarial images for variational autoencoders"

Connected through hyperedge HE-4 (induced by source paper "generative adversarial networks an overview"):
  with members:
    - "adversarial variational bayes unifying variational autoencoders and generative adversarial networks"
    - "the numerics of gans"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved training of wasserstein gans"
    - "precise recovery of latent vectors from generative adversarial networks"
    - "generalization and equilibrium in generative adversarial nets gans"
    - "precomputed real time texture synthesis with markovian generative adversarial networks"
    - "neural photo editing with introspective adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "clustergan latent space clustering in generative adversarial networks"):
  with members:
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "deep subspace clustering networks"
    - "improved training of wasserstein gans"
    - "the robust manifold defense adversarial training using generative models"
    - "precise recovery of latent vectors from generative adversarial networks"
    - "adversarial feature learning"
    - "towards k means friendly spaces simultaneous deep learning and clustering"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"

Connected through hyperedge HE-6 (induced by source paper "correction by projection denoising images with generative adversarial networks"):
  with members:
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "image denoising via cnns an adversarial approach"
    - "unrolled generative adversarial networks"
    - "adversarial feature learning"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "understanding deep image representations by inverting them"
    - "deep learning face attributes in the wild"

Connected through hyperedge HE-7 (induced by source paper "towards recovery of conditional vectors from conditional generative adversarial networks"):
  with members:
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "precise recovery of latent vectors from generative adversarial networks"
    - "precomputed real time texture synthesis with markovian generative adversarial networks"
    - "nips 2016 tutorial generative adversarial networks"
    - "improved techniques for training gans"
    - "generative adversarial text to image synthesis"
    - "adversarial feature learning"
    - "image to image translation with conditional adversarial networks"

Connected through hyperedge HE-8 (induced by source paper "learning inverse mapping by autoencoder based generative adversarial nets"):
  with members:
    - "unsupervised image to image translation with generative adversarial networks"
    - "invertible conditional gans for image editing"
    - "adversarial feature learning"
    - "image to image translation with conditional adversarial networks"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "deep learning face attributes in the wild"

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)