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 networks for time series continuous and discrete open data"
  Abstract: Open data plays a fundamental role in the 21th century by stimulating economic growth and by enabling more transparent and inclusive societies. However, it is always difficult to create new high-quality datasets with the required privacy guarantees for many use cases. This paper aims at creating a framework for releasing new open data while protecting the individuality of the users through a strict definition of privacy called differential privacy. Unlike previous work, this paper provides a framework for privacy preserving data publishing that can be easily adapted to different use cases, from the generation of time-series to continuous data, and discrete data; no previous work has focused on the later class. Indeed, many use cases expose discrete data or at least a combination between categorical and numerical values. Thanks to the latest developments in deep learning and generative models, it is now possible to model rich-semantic data maintaining both the original distribution of the features and the correlations between them. The output of this framework is a deep network, namely a generator, able to create new data on demand. We demonstrate the efficiency of our approach on real datasets from the French public administration and classic benchmark datasets.

Connected through hyperedge HE-1 (induced by source paper "dp cgan differentially private synthetic data and label generation"):
  with members:
    - "differentially private generative adversarial network"
    - "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"
    - "data synthesis based on generative adversarial networks"
    - "subsampled r enyi differential privacy and analytical moments accountant"
    - "differentially private generative adversarial network"
    - "differentially private data generative models"
    - "renyi differential privacy"

Connected through hyperedge HE-3 (induced by source paper "privgen preserving privacy of sequences through data generation"):
  with members:
    - "privacy preserving prediction"
    - "understanding membership inferences on well generalized learning models"
    - "plausible deniability for privacy preserving data synthesis"
    - "k τ e anonymity towards privacy preserving publishing of spatiotemporal trajectory data"
    - "generating multi label discrete patient records using generative adversarial networks"
    - "individual differential privacy a utility preserving formulation of differential privacy guarantees"
    - "privacy risk in machine learning analyzing the connection to overfitting"
    - "privtree a differentially private algorithm for hierarchical decompositions"

Connected through hyperedge HE-4 (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"
    - "evaluating differentially private machine learning in practice"
    - "videoflow a flow based generative model for video"
    - "detecting bias with generative counterfactual face attribute augmentation"
    - "auditing data provenance in text generation models"

Connected through hyperedge HE-5 (induced by source paper "generating high fidelity synthetic time series datasets with doppelganger"):
  with members:
    - "infogan cr disentangling generative adversarial networks with contrastive regularizers"
    - "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-6 (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"
    - "private selection from private candidates"
    - "differentially private generative adversarial network"
    - "differentially private data generative models"
    - "generating artificial data for private deep learning"
    - "differentially private mixture of generative neural 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.CR(Cryptography and Security)