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 "differentially private mixed type data generation for unsupervised learning"):
  Abstract: In this work we introduce the DP-auto-GAN framework for synthetic data generation, which combines the low dimensional representation of autoencoders with the flexibility of Generative Adversarial Networks (GANs). This framework can be used to take in raw sensitive data, and privately train a model for generating synthetic data that will satisfy the same statistical properties as the original data. This learned model can be used to generate arbitrary amounts of publicly available synthetic data, which can then be freely shared due to the post-processing guarantees of differential privacy. Our framework is applicable to unlabeled mixed-type data, that may include binary, categorical, and real-valued data. We implement this framework on both unlabeled binary data (MIMIC-III) and unlabeled mixed-type data (ADULT). We also introduce new metrics for evaluating the quality of synthetic mixed-type data, particularly in unsupervised settings.

Includes members:
  Member "dp cgan differentially private synthetic data and label generation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "differentially private mean embeddings with random features dp merf for simple practical synthetic data generation")
  Member "differentially private learning with adaptive clipping":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "removing disparate impact of differentially private stochastic gradient descent on model accuracy")
  Member "differentially private model publishing for deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "membership inference attacks against object detection models")
  Member "differentially private generative adversarial networks for time series continuous and discrete open data":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "dp cgan differentially private synthetic data and label generation")
  Member "private selection from private candidates":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "guidelines for implementing and auditing differentially private systems")
  Member "differentially private generative adversarial network":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "dp cgan differentially private synthetic data and label generation")
  Member "differentially private data generative models":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "dp cgan differentially private synthetic data and label generation")
  Member "generating artificial data for private deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "federated learning with bayesian differential privacy")

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)