human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "differentially private generative adversarial network":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "dp cgan differentially private synthetic data and label generation")
  Member "self normalizing neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "soft root sign activation function")
  Member "plausible deniability for privacy preserving data synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "privgen preserving privacy of sequences through data generation")
  Member "context aware generative adversarial privacy":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "local information privacy and its application to privacy preserving data aggregation")
  Member "improved training of wasserstein gans":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "deep models under the gan information leakage from collaborative deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "trends and advancements in deep neural network communication")
  Member "concentrated differential privacy":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "privately learning markov random fields")
  Member "membership inference attacks against machine learning models":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "the future of digital health with federated learning")

Question: Please tell me which class the source paper 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)