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: "infogan cr disentangling generative adversarial networks with contrastive regularizers"
  Abstract: Training disentangled representations with generative adversarial networks (GANs) remains challenging, with leading implementations failing to achieve comparable performance to Variational Autoencoder (VAE)-based methods. After $\beta$-VAE and FactorVAE discovered that regularizing the total correlation of the latent vectors promotes disentanglement, numerous VAE-based methods emerged. Such a discovery has yet to be made for GANs, and reported disentanglement scores of GAN-based methods are significantly inferior to VAE-based methods on benchmark datasets. To this end, we propose a novel regularizer that achieves higher disentanglement scores than state-of-the-art VAE- and GAN-based approaches. The proposed contrastive regularizer is inspired by a natural notion of disentanglement: latent traversal. Latent traversal refers to generating images by varying one latent code while fixing the rest. We turn this intuition into a regularizer by adding a discriminator that detects how the latent codes are coupled together, in paired examples. Numerical experiments show that this approach improves upon competing state-of-the-art approaches on benchmark datasets.

Connected through hyperedge HE-1 (induced by source paper "high fidelity synthesis with disentangled representation"):
  with members:
    - "hologan unsupervised learning of 3d representations from natural images"
    - "weakly supervised disentanglement by pairwise similarities"
    - "learning disentangled representations with reference based variational autoencoders"
    - "disentangling factors of variation using few labels"
    - "spatial broadcast decoder a simple architecture for learning disentangled representations in vaes"
    - "on the fairness of disentangled representations"
    - "flexibly fair representation learning by disentanglement"
    - "oogan disentangling gan with one hot sampling and orthogonal regularization"

Connected through hyperedge HE-2 (induced by source paper "semi supervised stylegan for disentanglement learning"):
  with members:
    - "hologan unsupervised learning of 3d representations from natural images"
    - "disentangling factors of variation using few labels"
    - "mixmatch a holistic approach to semi supervised learning"
    - "learning deep disentangled embeddings with the f statistic loss"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "isolating sources of disentanglement in variational autoencoders"
    - "a style based generator architecture for generative adversarial networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"

Connected through hyperedge HE-3 (induced by source paper "generating high fidelity synthetic time series datasets with doppelganger"):
  with members:
    - "differentially private generative adversarial networks for time series continuous and discrete open data"
    - "medical image synthesis for data augmentation and anonymization using generative adversarial networks"
    - "synthetic data augmentation using gan for improved liver lesion classification"
    - "a statistical recurrent model on the manifold of symmetric positive definite matrices"
    - "differentially private generative adversarial network"
    - "progressive growing of gans for improved quality stability and variation"
    - "generating multi label discrete patient records using generative adversarial networks"
    - "gated orthogonal recurrent units on learning to forget"

Connected through hyperedge HE-4 (induced by source paper "oogan disentangling gan with one hot sampling and orthogonal regularization"):
  with members:
    - "learning feature to feature translator by alternating back propagation for zero shot learning"
    - "entropy and mutual information in models of deep neural networks"
    - "semantic guided multi attention localization for zero shot learning"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "spectral normalization for generative adversarial networks"
    - "can we gain more from orthogonality regularizations in training deep cnns"
    - "isolating sources of disentanglement in variational autoencoders"
    - "a style based generator architecture for generative adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "deformable generator network unsupervised disentanglement of appearance and geometry"):
  with members:
    - "motion based generator model unsupervised disentanglement of appearance trackable and intrackable motions in dynamic patterns"
    - "on the fairness of disentangled representations"
    - "flexibly fair representation learning by disentanglement"
    - "multimodal unsupervised image to image translation"
    - "automatic chemical design using a data driven continuous representation of molecules"
    - "isolating sources of disentanglement in variational autoencoders"
    - "large scale gan training for high fidelity natural image synthesis"
    - "unsupervised disentangled representation learning with analogical relations"

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)