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: "generalization in generative adversarial networks a novel perspective from privacy protection"
  Abstract: In this paper, we aim to understand the generalization properties of generative adversarial networks (GANs) from a new perspective of privacy protection. Theoretically, we prove that a differentially private learning algorithm used for training the GAN does not overfit to a certain degree, i.e., the generalization gap can be bounded. Moreover, some recent works, such as the Bayesian GAN, can be re-interpreted based on our theoretical insight from privacy protection. Quantitatively, to evaluate the information leakage of well-trained GAN models, we perform various membership attacks on these models. The results show that previous Lipschitz regularization techniques are effective in not only reducing the generalization gap but also alleviating the information leakage of the training dataset.

Connected through hyperedge HE-1 (induced by source paper "input perturbation a new paradigm between central and local differential privacy"):
  with members:
    - "efficiently estimating erdos renyi graphs with node differential privacy"
    - "differentially private bayesian linear regression"
    - "collecting and analyzing multidimensional data with local differential privacy"
    - "differentially private empirical risk minimization revisited faster and more general"
    - "noninteractive locally private learning of linear models via polynomial approximations"
    - "efficient private erm for smooth objectives"
    - "concentrated differential privacy simplifications extensions and lower bounds"
    - "membership inference attacks against machine learning models"

Connected through hyperedge HE-2 (induced by source paper "differentially private erm based on data perturbation"):
  with members:
    - "differentially private bayesian linear regression"
    - "dp lssgd a stochastic optimization method to lift the utility in privacy preserving erm"
    - "differentially private empirical risk minimization revisited faster and more general"
    - "efficient private erm for smooth objectives"
    - "collect at once use effectively making non interactive locally private learning possible"
    - "adaptive laplace mechanism differential privacy preservation in deep learning"
    - "bolt on differential privacy for scalable stochastic gradient descent based analytics"
    - "membership inference attacks against machine learning models"

Connected through hyperedge HE-3 (induced by source paper "alleviation for gradient exploding in gans fake can be real"):
  with members:
    - "large scale adversarial representation learning"
    - "improving generalization and stability of generative adversarial networks"
    - "spectral normalization for generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "a style based generator architecture for generative adversarial networks"
    - "which training methods for gans do actually converge"
    - "on self modulation for generative adversarial networks"
    - "multi agent diverse generative adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "characterizing membership privacy in stochastic gradient langevin dynamics"):
  with members:
    - "stolen memories leveraging model memorization for calibrated white box membership inference"
    - "p3sgd patient privacy preserving sgd for regularizing deep cnns in pathological image classification"
    - "dropblock a regularization method for convolutional networks"
    - "generalization error bounds for noisy iterative algorithms"
    - "understanding membership inferences on well generalized learning models"
    - "generalization bounds of sgld for non convex learning two theoretical viewpoints"
    - "privacy risk in machine learning analyzing the connection to overfitting"
    - "wide residual networks"

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)