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 data generative models"
  Abstract: Deep neural networks (DNNs) have recently been widely adopted in various applications, and such success is largely due to a combination of algorithmic breakthroughs, computation resource improvements, and access to a large amount of data. However, the large-scale data collections required for deep learning often contain sensitive information, therefore raising many privacy concerns. Prior research has shown several successful attacks in inferring sensitive training data information, such as model inversion, membership inference, and generative adversarial networks (GAN) based leakage attacks against collaborative deep learning. In this paper, to enable learning efficiency as well as to generate data with privacy guarantees and high utility, we propose a differentially private autoencoder-based generative model (DP-AuGM) and a differentially private variational autoencoder-based generative model (DP-VaeGM). We evaluate the robustness of two proposed models. We show that DP-AuGM can effectively defend against the model inversion, membership inference, and GAN-based attacks. We also show that DP-VaeGM is robust against the membership inference attack. We conjecture that the key to defend against the model inversion and GAN-based attacks is not due to differential privacy but the perturbation of training data. Finally, we demonstrate that both DP-AuGM and DP-VaeGM can be easily integrated with real-world machine learning applications, such as machine learning as a service and federated learning, which are otherwise threatened by the membership inference attack and the GAN-based attack, respectively.

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 generative adversarial network"
    - "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 generative adversarial network"
    - "renyi differential privacy"

Connected through hyperedge HE-3 (induced by source paper "differentially private mixed type data generation for unsupervised learning"):
  with members:
    - "dp cgan differentially private synthetic data and label generation"
    - "differentially private learning with adaptive clipping"
    - "differentially private model publishing for deep learning"
    - "differentially private generative adversarial networks for time series continuous and discrete open data"
    - "private selection from private candidates"
    - "differentially private generative adversarial network"
    - "generating artificial data for private deep learning"
    - "differentially private mixture of generative neural networks"

Connected through hyperedge HE-4 (induced by source paper "privacy leakage avoidance with switching ensembles"):
  with members:
    - "privacy risks of explaining machine learning models"
    - "membership model inversion attacks for deep networks"
    - "ml leaks model and data independent membership inference attacks and defenses on machine learning models"
    - "billion scale similarity search with gpus"
    - "practical black box attacks against machine learning"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"
    - "stealing machine learning models via prediction apis"
    - "membership inference attacks against machine learning models"

Connected through hyperedge HE-5 (induced by source paper "matrix sketching for secure collaborative machine learning"):
  with members:
    - "gradient descent with compressed iterates"
    - "exploiting unintended feature leakage in collaborative learning"
    - "deep models under the gan information leakage from collaborative deep learning"
    - "communication efficient learning of deep networks from decentralized data"
    - "the johnson lindenstrauss transform itself preserves differential privacy"
    - "fast approximation of matrix coherence and statistical leverage"
    - "randomized algorithms for matrices and data"
    - "faster least squares approximation"

Connected through hyperedge HE-6 (induced by source paper "an empirical and comparative analysis of data valuation with scalable algorithms"):
  with members:
    - "towards efficient data valuation based on the shapley value"
    - "detecting backdoor attacks on deep neural networks by activation clustering"
    - "turning your weakness into a strength watermarking deep neural networks by backdooring"
    - "deep residual learning for image recognition"

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.CR(Cryptography and Security)