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: "reconstruction of three dimensional porous media using generative adversarial neural networks"
  Abstract: To evaluate the variability of multi-phase flow properties of porous media at the pore scale, it is necessary to acquire a number of representative samples of the void-solid structure. While modern x-ray computer tomography has made it possible to extract three-dimensional images of the pore space, assessment of the variability in the inherent material properties is often experimentally not feasible. We present a novel method to reconstruct the solid-void structure of porous media by applying a generative neural network that allows an implicit description of the probability distribution represented by three-dimensional image datasets. We show, by using an adversarial learning approach for neural networks, that this method of unsupervised learning is able to generate representative samples of porous media that honor their statistics. We successfully compare measures of pore morphology, such as the Euler characteristic, two-point statistics and directional single-phase permeability of synthetic realizations with the calculated properties of a bead pack, Berea sandstone, and Ketton limestone. Results show that GANs can be used to reconstruct high-resolution three-dimensional images of porous media at different scales that are representative of the morphology of the images used to train the neural network. The fully convolutional nature of the trained neural network allows the generation of large samples while maintaining computational efficiency. Compared to classical stochastic methods of image reconstruction, the implicit representation of the learned data distribution can be stored and reused to generate multiple realizations of the pore structure very rapidly.

Connected through hyperedge HE-1 (induced by source paper "user controllable multi texture synthesis with generative adversarial networks"):
  with members:
    - "reconstruction of 3d porous media from 2d slices"
    - "improving generalization and stability of generative adversarial networks"
    - "non stationary texture synthesis by adversarial expansion"
    - "spectral normalization for generative adversarial networks"
    - "texturegan controlling deep image synthesis with texture patches"
    - "diversified texture synthesis with feed forward networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved texture networks maximizing quality and diversity in feed forward stylization and texture synthesis"

Connected through hyperedge HE-2 (induced by source paper "deep fluids a generative network for parameterized fluid simulations"):
  with members:
    - "image inpainting for irregular holes using partial convolutions"
    - "latent space physics towards learning the temporal evolution of fluid flow"
    - "began boundary equilibrium generative adversarial networks"
    - "deep scattering rendering atmospheric clouds with radiance predicting neural networks"
    - "liquid splash modeling with neural networks"
    - "progressive growing of gans for improved quality stability and variation"
    - "deep learning the physics of transport phenomena"
    - "interpolations of smoke and liquid simulations"

Connected through hyperedge HE-3 (induced by source paper "deepflow history matching in the space of deep generative models"):
  with members:
    - "cyclical stochastic gradient mcmc for bayesian deep learning"
    - "cgans with projection discriminator"
    - "analyzing inverse problems with invertible neural networks"
    - "probabilistic semantic inpainting with pixel constrained cnns"
    - "adversarial regularizers in inverse problems"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "sampling generative networks"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-4 (induced by source paper "reconstruction of 3d porous media from 2d slices"):
  with members:
    - "latent convolutional models"
    - "large scale shape retrieval with sparse 3d convolutional neural networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"
    - "conditional generative adversarial nets"
    - "numerical analysis of topological characteristics of three dimensional geological models of oil and gas fields"

Connected through hyperedge HE-5 (induced by source paper "tempogan a temporally coherent volumetric gan for super resolution fluid flow"):
  with members:
    - "began boundary equilibrium generative adversarial networks"
    - "deep scattering rendering atmospheric clouds with radiance predicting neural networks"
    - "deep learning for physical processes incorporating prior scientific knowledge"
    - "liquid splash modeling with neural networks"
    - "progressive growing of gans for improved quality stability and variation"
    - "deep learning the physics of transport phenomena"
    - "deep photo style transfer"
    - "enhanced deep residual networks for single image super resolution"

Connected through hyperedge HE-6 (induced by source paper "how good is my gan"):
  with members:
    - "cgans with projection discriminator"
    - "predicting rapid fire growth flashover using conditional generative adversarial networks"
    - "synthetic data augmentation using gan for improved liver lesion classification"
    - "spectral normalization for generative adversarial networks"
    - "low shot learning from imaginary data"
    - "progressive growing of gans for improved quality stability and variation"
    - "good semi supervised learning that requires a bad gan"
    - "camera style adaptation for person re identification"

Connected through hyperedge HE-7 (induced by source paper "well how accurate is it a study of deep learning methods for reynolds averaged navier stokes simulations"):
  with members:
    - "hidden fluid mechanics a navier stokes informed deep learning framework for assimilating flow visualization data"
    - "liquid splash modeling with neural networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "generating liquid simulations with deformation aware neural networks"
    - "long term forecasting using higher order tensor rnns"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "adam a method for stochastic optimization"
    - "conditional generative adversarial nets"

Connected through hyperedge HE-8 (induced by source paper "stochastic reconstruction of an oolitic limestone by generative adversarial networks"):
  with members:
    - "on convergence and stability of gans"
    - "began boundary equilibrium generative adversarial networks"
    - "improved training of wasserstein gans"
    - "do gans actually learn the distribution an empirical study"
    - "amortised map inference for image super resolution"
    - "energy based generative adversarial network"
    - "real time single image and video super resolution using an efficient sub pixel convolutional neural network"
    - "semantic image inpainting with deep generative models"

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)