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 "input perturbation a new paradigm between central and local differential privacy"):
  Abstract: Traditionally, there are two models on differential privacy: the central model and the local model. The central model focuses on the machine learning model and the local model focuses on the training data. In this paper, we study the \textit{input perturbation} method in differentially private empirical risk minimization (DP-ERM), preserving privacy of the central model. By adding noise to the original training data and training with the `perturbed data', we achieve ($\epsilon$,$\delta$)-differential privacy on the final model, along with some kind of privacy on the original data. We observe that there is an interesting connection between the local model and the central model: the perturbation on the original data causes the perturbation on the gradient, and finally the model parameters. This observation means that our method builds a bridge between local and central model, protecting the data, the gradient and the model simultaneously, which is more superior than previous central methods. Detailed theoretical analysis and experiments show that our method achieves almost the same (or even better) performance as some of the best previous central methods with more protections on privacy, which is an attractive result. Moreover, we extend our method to a more general case: the loss function satisfies the Polyak-Lojasiewicz condition, which is more general than strong convexity, the constraint on the loss function in most previous work.

Includes members:
  Member "generalization in generative adversarial networks a novel perspective from privacy protection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "differentially private erm based on data perturbation")
  Member "efficiently estimating erdos renyi graphs with node differential privacy":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "private identity testing for high dimensional distributions")
  Member "differentially private bayesian linear regression":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "differentially private erm based on data perturbation")
  Member "collecting and analyzing multidimensional data with local differential privacy":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "locally private distributed reinforcement learning")
  Member "differentially private empirical risk minimization revisited faster and more general":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "differentially private erm based on data perturbation")
  Member "noninteractive locally private learning of linear models via polynomial approximations":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "interaction is necessary for distributed learning with privacy or communication constraints")
  Member "efficient private erm for smooth objectives":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "private and communication efficient edge learning a sparse differential gaussian masking distributed sgd approach")
  Member "concentrated differential privacy simplifications extensions and lower bounds":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "reasoning about generalization via conditional mutual information")

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)