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: "generating artificial data for private deep learning"
  Abstract: In this paper, we propose generating artificial data that retain statistical properties of real data as the means of providing privacy with respect to the original dataset. We use generative adversarial network to draw privacy-preserving artificial data samples and derive an empirical method to assess the risk of information disclosure in a differential-privacy-like way. Our experiments show that we are able to generate artificial data of high quality and successfully train and validate machine learning models on this data while limiting potential privacy loss.

Connected through hyperedge HE-1 (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"
    - "differentially private data generative models"
    - "differentially private mixture of generative neural networks"

Connected through hyperedge HE-2 (induced by source paper "federated learning with bayesian differential privacy"):
  with members:
    - "improved accounting for differentially private learning"
    - "towards federated learning at scale system design"
    - "bayesian differential privacy for machine learning"
    - "privacy amplification by subsampling tight analyses via couplings and divergences"
    - "differentially private federated learning a client level perspective"
    - "learning differentially private recurrent language models"
    - "renyi differential privacy"
    - "deep models under the gan information leakage from collaborative deep learning"

Connected through hyperedge HE-3 (induced by source paper "bayesian differential privacy for machine learning"):
  with members:
    - "privacy amplification by subsampling tight analyses via couplings and divergences"
    - "learning differentially private recurrent language models"
    - "renyi differential privacy"
    - "deep models under the gan information leakage from collaborative deep learning"
    - "concentrated differential privacy simplifications extensions and lower bounds"
    - "concentrated differential privacy"
    - "membership inference attacks against machine learning models"
    - "on the relation between identifiability differential privacy and mutual information privacy"

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)