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: "differentially private generative adversarial network"
  Abstract: Generative Adversarial Network (GAN) and its variants have recently attracted intensive research interests due to their elegant theoretical foundation and excellent empirical performance as generative models. These tools provide a promising direction in the studies where data availability is limited. One common issue in GANs is that the density of the learned generative distribution could concentrate on the training data points, meaning that they can easily remember training samples due to the high model complexity of deep networks. This becomes a major concern when GANs are applied to private or sensitive data such as patient medical records, and the concentration of distribution may divulge critical patient information. To address this issue, in this paper we propose a differentially private GAN (DPGAN) model, in which we achieve differential privacy in GANs by adding carefully designed noise to gradients during the learning procedure. We provide rigorous proof for the privacy guarantee, as well as comprehensive empirical evidence to support our analysis, where we demonstrate that our method can generate high quality data points at a reasonable privacy level.

Connected through hyperedge HE-1 (induced by source paper "dp cgan differentially private synthetic data and label generation"):
  with members:
    - "differentially private generative adversarial networks for time series continuous and discrete open data"
    - "differentially private data generative models"
    - "face aging with conditional generative adversarial networks"
    - "generating and designing dna with deep generative models"
    - "logan membership inference attacks against generative models"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "understanding deep learning requires rethinking generalization"
    - "concentrated differential privacy"

Connected through hyperedge HE-2 (induced by source paper "differentially private mean embeddings with random features dp merf for simple practical synthetic data generation"):
  with members:
    - "dp cgan differentially private synthetic data and label generation"
    - "differentially private mixed type data generation for unsupervised learning"
    - "differentially private distributed data summarization under covariate shift"
    - "differentially private generative adversarial networks for time series continuous and discrete open data"
    - "data synthesis based on generative adversarial networks"
    - "subsampled r enyi differential privacy and analytical moments accountant"
    - "differentially private data generative models"
    - "renyi differential privacy"

Connected through hyperedge HE-3 (induced by source paper "protecting gans against privacy attacks by preventing overfitting"):
  with members:
    - "gan leaks a taxonomy of membership inference attacks against gans"
    - "generative adversarial network in medical imaging a review"
    - "deepobfuscator adversarial training framework for privacy preserving image classification"
    - "privacy preserving machine learning threats and solutions"
    - "logan membership inference attacks against generative models"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "the differentially private lottery ticket mechanism"):
  with members:
    - "the secret sharer measuring unintended neural network memorization extracting secrets"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "learning differentially private recurrent language models"
    - "machine learning models that remember too much"
    - "calibrating data to sensitivity in private data analysis"

Connected through hyperedge HE-5 (induced by source paper "gan leaks a taxonomy of membership inference attacks against gans"):
  with members:
    - "texture mixer a network for controllable synthesis and interpolation of texture"
    - "non stationary texture synthesis by adversarial expansion"
    - "exploiting unintended feature leakage in collaborative learning"
    - "attributing fake images to gans learning and analyzing gan fingerprints"
    - "privacy protective gan for face de identification"
    - "the secret sharer measuring unintended neural network memorization extracting secrets"
    - "large scale gan training for high fidelity natural image synthesis"
    - "the unreasonable effectiveness of deep features as a perceptual metric"

Connected through hyperedge HE-6 (induced by source paper "generative models for effective ml on private decentralized datasets"):
  with members:
    - "dp cgan differentially private synthetic data and label generation"
    - "taxonomy of real faults in deep learning systems"
    - "reconstruction and membership inference attacks against generative models"
    - "towards federated learning at scale system design"
    - "differentially private generative adversarial networks for time series continuous and discrete open data"
    - "evaluating differentially private machine learning in practice"
    - "videoflow a flow based generative model for video"
    - "detecting bias with generative counterfactual face attribute augmentation"

Connected through hyperedge HE-7 (induced by source paper "generating high fidelity synthetic time series datasets with doppelganger"):
  with members:
    - "infogan cr disentangling generative adversarial networks with contrastive regularizers"
    - "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"
    - "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-8 (induced by source paper "invert and defend model based approximate inversion of generative adversarial networks for secure inference"):
  with members:
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "spectral normalization for generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "a style based generator architecture for generative adversarial networks"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "it takes only two adversarial generator encoder 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)