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: "a geometric view of optimal transportation and generative model"
  Abstract: In this work, we show the intrinsic relations between optimal transportation and convex geometry, especially the variational approach to solve Alexandrov problem: constructing a convex polytope with prescribed face normals and volumes. This leads to a geometric interpretation to generative models, and leads to a novel framework for generative models. By using the optimal transportation view of GAN model, we show that the discriminator computes the Kantorovich potential, the generator calculates the transportation map. For a large class of transportation costs, the Kantorovich potential can give the optimal transportation map by a close-form formula. Therefore, it is sufficient to solely optimize the discriminator. This shows the adversarial competition can be avoided, and the computational architecture can be simplified. Preliminary experimental results show the geometric method outperforms WGAN for approximating probability measures with multiple clusters in low dimensional space.

Connected through hyperedge HE-1 (induced by source paper "knowledge representing efficient sparse representation of prior knowledge for knowledge distillation"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "knowledge distillation with adversarial samples supporting decision boundary"
    - "learning strict identity mappings in deep residual networks"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "data driven sparse structure selection for deep neural networks"
    - "thinet a filter level pruning method for deep neural network compression"
    - "like what you like knowledge distill via neuron selectivity transfer"
    - "a survey of model compression and acceleration for deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "optimal transport mapping via input convex neural networks"):
  with members:
    - "on scalable and efficient computation of large scale optimal transport"
    - "subspace detours building transport plans that are optimal on subspace projections"
    - "adversarial computation of optimal transport maps"
    - "mode collapse and regularity of optimal transportation maps"
    - "improving the improved training of wasserstein gans a consistency term and its dual effect"
    - "generative modeling using the sliced wasserstein distance"
    - "pacgan the power of two samples in generative adversarial networks"
    - "mmd gan towards deeper understanding of moment matching network"

Connected through hyperedge HE-3 (induced by source paper "adversarial computation of optimal transport maps"):
  with members:
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "spectral normalization for generative adversarial networks"
    - "on the convergence and robustness of training gans with regularized optimal transport"
    - "improving gans using optimal transport"
    - "on convergence and stability of gans"
    - "solving approximate wasserstein gans to stationarity"
    - "guiding the one to one mapping in cyclegan via optimal transport"
    - "pacgan the power of two samples in generative adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "optimal transport based generative autoencoders"):
  with members:
    - "large scale gan training for high fidelity natural image synthesis"
    - "on convergence and stability of gans"
    - "latent space optimal transport for generative models"
    - "which training methods for gans do actually converge"
    - "began boundary equilibrium generative adversarial networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-5 (induced by source paper "meta particle flow for sequential bayesian inference"):
  with members:
    - "monge amp ere flow for generative modeling"
    - "deep sets"
    - "stable architectures for deep neural networks"
    - "neural ordinary differential equations"
    - "ffjord free form continuous dynamics for scalable reversible generative models"
    - "filtering variational objectives"
    - "learning combinatorial optimization algorithms over graphs"
    - "learning to learn by gradient descent by gradient descent"

Connected through hyperedge HE-6 (induced by source paper "fuzzy logic interpretation of artificial neural networks"):
  with members:
    - "geometric understanding of deep learning"
    - "structurally sensitive multi scale deep neural network for low dose ct denoising"
    - "exact and consistent interpretation for piecewise linear neural networks a closed form solution"
    - "a new type of neurons for machine learning"
    - "interpreting deep visual representations via network dissection"
    - "revisit fuzzy neural network demystifying batch normalization and relu with generalized hamming network"
    - "visualizing the loss landscape of neural nets"
    - "sharp minima can generalize for deep nets"

Connected through hyperedge HE-7 (induced by source paper "latent space optimal transport for generative models"):
  with members:
    - "spectral normalization for generative adversarial networks"
    - "began boundary equilibrium generative adversarial networks"
    - "adversarial variational bayes unifying variational autoencoders and generative adversarial networks"
    - "generative face completion"
    - "unsupervised histopathology image synthesis"
    - "creativity generating diverse questions using variational autoencoders"
    - "neural face editing with intrinsic image disentangling"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-8 (induced by source paper "on the discrimination generalization tradeoff in gans"):
  with members:
    - "spectral normalization for generative adversarial networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "comparison of maximum likelihood and gan based training of real nvps"
    - "the numerics of gans"
    - "generative adversarial mapping networks"
    - "approximation and convergence properties of generative adversarial learning"
    - "on unifying deep generative models"
    - "spectrally normalized margin bounds for neural networks"

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)