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: "improving gans using optimal transport"
  Abstract: We present Optimal Transport GAN (OT-GAN), a variant of generative adversarial nets minimizing a new metric measuring the distance between the generator distribution and the data distribution. This metric, which we call mini-batch energy distance, combines optimal transport in primal form with an energy distance defined in an adversarially learned feature space, resulting in a highly discriminative distance function with unbiased mini-batch gradients. Experimentally we show OT-GAN to be highly stable when trained with large mini-batches, and we present state-of-the-art results on several popular benchmark problems for image generation.

Connected through hyperedge HE-1 (induced by source paper "probabilistic future prediction for video scene understanding"):
  with members:
    - "precog prediction conditioned on goals in visual multi agent settings"
    - "model based reinforcement learning for atari"
    - "adversarial video generation on complex datasets"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "learning latent dynamics for planning from pixels"
    - "learning plannable representations with causal infogan"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "visual foresight model based deep reinforcement learning for vision based robotic control"

Connected through hyperedge HE-2 (induced by source paper "blur noise and compression robust generative adversarial networks"):
  with members:
    - "analyzing and improving the image quality of stylegan"
    - "noise robust generative adversarial networks"
    - "high quality self supervised deep image denoising"
    - "unsupervised domain specific deblurring via disentangled representations"
    - "noise2self blind denoising by self supervision"
    - "diversity sensitive conditional generative adversarial networks"
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "toward convolutional blind denoising of real photographs"

Connected through hyperedge HE-3 (induced by source paper "gans may have no nash equilibria"):
  with members:
    - "what is local optimality in nonconvex nonconcave minimax optimization"
    - "lipschitz generative adversarial nets"
    - "on gradient descent ascent for nonconvex concave minimax problems"
    - "policy optimization provably converges to nash equilibria in zero sum linear quadratic games"
    - "on finding local nash equilibria and only local nash equilibria in zero sum games"
    - "sgd learns one layer networks in wgans"
    - "on solving minimax optimization locally a follow the ridge approach"
    - "convergence of learning dynamics in stackelberg games"

Connected through hyperedge HE-4 (induced by source paper "adaptive density estimation for generative models"):
  with members:
    - "improving generalization and stability of generative adversarial networks"
    - "cgans with projection discriminator"
    - "how good is my gan"
    - "spectral normalization for generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "generating high fidelity images with subscale pixel networks and multidimensional upscaling"
    - "fast generation for convolutional autoregressive models"
    - "modulating early visual processing by language"

Connected through hyperedge HE-5 (induced by source paper "joint wasserstein distribution matching"):
  with members:
    - "unsupervised video to video translation"
    - "dual reconstruction nets for image super resolution with gradient sensitive loss"
    - "improving shape deformation in unsupervised image to image translation"
    - "jointgan multi domain joint distribution learning with generative adversarial nets"
    - "unsupervised attention guided image to image translation"
    - "unsupervised image to image translation with stacked cycle consistent adversarial networks"
    - "deep view morphing"
    - "quo vadis action recognition a new model and the kinetics dataset"

Connected through hyperedge HE-6 (induced by source paper "connecting gans and mfgs"):
  with members:
    - "smoothness and stability in gans"
    - "on the convergence and robustness of training gans with regularized optimal transport"
    - "f gans in an information geometric nutshell"
    - "improved training of wasserstein gans"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "contextual lstm clstm models for large scale nlp tasks"
    - "semantic segmentation using adversarial networks"
    - "generative adversarial text to image synthesis"

Connected through hyperedge HE-7 (induced by source paper "co generation with gans using ais based hmc"):
  with members:
    - "max sliced wasserstein distance and its use for gans"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "medical image synthesis for data augmentation and anonymization using generative adversarial networks"
    - "multimodal unsupervised image to image translation"
    - "generative modeling using the sliced wasserstein distance"
    - "xgan unsupervised image to image translation for many to many mappings"
    - "mcgan mean and covariance feature matching gan"
    - "style transfer from non parallel text by cross alignment"

Connected through hyperedge HE-8 (induced by source paper "noise robust generative adversarial networks"):
  with members:
    - "generating diverse high fidelity images with vq vae 2"
    - "noise2self blind denoising by self supervision"
    - "diversity sensitive conditional generative adversarial networks"
    - "revisiting self supervised visual representation learning"
    - "high fidelity image generation with fewer labels"
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "self supervised gans via auxiliary rotation loss"
    - "toward convolutional blind denoising of real photographs"

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)