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 mean embeddings with random features dp merf for simple practical synthetic data generation"):
  Abstract: We present a differentially private data generation paradigm using random feature representations of kernel mean embeddings when comparing the distribution of true data with that of synthetic data. We exploit the random feature representations for two important benefits. First, we require a very low privacy cost for training deep generative models. This is because unlike kernel-based distance metrics that require computing the kernel matrix on all pairs of true and synthetic data points, we can detach the data-dependent term from the term solely dependent on synthetic data. Hence, we need to perturb the data-dependent term once-for-all and then use it until the end of the generator training. Second, we can obtain an analytic sensitivity of the kernel mean embedding as the random features are norm bounded by construction. This removes the necessity of hyperparameter search for a clipping norm to handle the unknown sensitivity of an encoder network when dealing with high-dimensional data. We provide several variants of our algorithm, differentially private mean embeddings with random features (DP-MERF) to generate (a) heterogeneous tabular data, (b) input features and corresponding labels jointly; and (c) high-dimensional data. Our algorithm achieves better privacy-utility trade-offs than existing methods tested on several datasets.

Includes members:
  Member "dp cgan differentially private synthetic data and label generation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "generative models for effective ml on private decentralized datasets")
  Member "differentially private mixed type data generation for unsupervised learning":
    also connected through other hyperedges:
      (none)
  Member "differentially private distributed data summarization under covariate shift":
    also connected through other hyperedges:
      (none)
  Member "differentially private generative adversarial networks for time series continuous and discrete open data":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "dp cgan differentially private synthetic data and label generation")
  Member "data synthesis based on generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "approximate query processing using deep generative models")
  Member "subsampled r enyi differential privacy and analytical moments accountant":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "differentially private generative adversarial network":
    also connected through other hyperedges:
      - Hyperedge HE-5 (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-6 (induced by source paper "dp cgan differentially private synthetic data and label generation")

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)