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: "sliced wasserstein generative models"
  Abstract: In generative modeling, the Wasserstein distance (WD) has emerged as a useful metric to measure the discrepancy between generated and real data distributions. Unfortunately, it is challenging to approximate the WD of high-dimensional distributions. In contrast, the sliced Wasserstein distance (SWD) factorizes high-dimensional distributions into their multiple one-dimensional marginal distributions and is thus easier to approximate. In this paper, we introduce novel approximations of the primal and dual SWD. Instead of using a large number of random projections, as it is done by conventional SWD approximation methods, we propose to approximate SWDs with a small number of parameterized orthogonal projections in an end-to-end deep learning fashion. As concrete applications of our SWD approximations, we design two types of differentiable SWD blocks to equip modern generative frameworks---Auto-Encoders (AE) and Generative Adversarial Networks (GAN). In the experiments, we not only show the superiority of the proposed generative models on standard image synthesis benchmarks, but also demonstrate the state-of-the-art performance on challenging high resolution image and video generation in an unsupervised manner.

Connected through hyperedge HE-1 (induced by source paper "fdftnet facing off fake images using fake detection fine tuning network"):
  with members:
    - "faceforensics learning to detect manipulated facial images"
    - "few shot adversarial learning of realistic neural talking head models"
    - "searching for mobilenetv3"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "a quantization friendly separable convolution for mobilenets"
    - "faceforensics a large scale video dataset for forgery detection in human faces"
    - "meta transfer learning for few shot learning"
    - "a style based generator architecture for generative adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "max sliced wasserstein distance and its use for gans"):
  with members:
    - "sliced wasserstein autoencoder an embarrassingly simple generative model"
    - "multimodal unsupervised image to image translation"
    - "improving gans using optimal transport"
    - "generative modeling using the sliced wasserstein distance"
    - "xgan unsupervised image to image translation for many to many mappings"
    - "which training methods for gans do actually converge"
    - "mcgan mean and covariance feature matching gan"
    - "began boundary equilibrium generative adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "towards visually explaining variational autoencoders"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "f vaegan d2 a feature generating framework for any shot learning"
    - "deep learning for anomaly detection a survey"
    - "gs3d an efficient 3d object detection framework for autonomous driving"
    - "ganomaly semi supervised anomaly detection via adversarial training"
    - "re identification with consistent attentive siamese networks"
    - "sharpen focus learning with attention separability and consistency"
    - "attention branch network learning of attention mechanism for visual explanation"

Connected through hyperedge HE-4 (induced by source paper "illegible text to readable text an image to image transformation using conditional sliced wasserstein adversarial networks"):
  with members:
    - "generalized sliced wasserstein distances"
    - "handwritten text segmentation via end to end learning of convolutional neural network"
    - "improving the improved training of wasserstein gans a consistency term and its dual effect"
    - "how good is my gan"
    - "progressive growing of gans for improved quality stability and variation"
    - "improved training of wasserstein gans"
    - "improved techniques for training gans"
    - "generative adversarial text to image synthesis"

Connected through hyperedge HE-5 (induced by source paper "subspace detours building transport plans that are optimal on subspace projections"):
  with members:
    - "subspace robust wasserstein distances"
    - "generative modeling using the sliced wasserstein distance"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "deep hashing network for unsupervised domain adaptation"
    - "optimal transport for domain adaptation"
    - "deep learning face attributes in the wild"
    - "regularized discrete optimal transport"

Connected through hyperedge HE-6 (induced by source paper "x ray sobolev variational auto encoders"):
  with members:
    - "max sliced wasserstein distance and its use for gans"
    - "generative modeling using the sliced wasserstein distance"
    - "sliced wasserstein distance for learning gaussian mixture models"
    - "adam a method for stochastic optimization"

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)