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: "performing co membership attacks against deep generative models"
  Abstract: In this paper we propose new membership attacks and new attack methods against deep generative models including Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs). Specifically, a membership attack is to check whether a given instance x was used in the training data or not. And a co-membership attack is to check whether the given bundle n instances were in the training, with the prior knowledge that the bundle was either entirely used in the training or none at all. Successful membership attacks can compromise privacy of training data when the generative model is published. Our main idea is to cast membership inference of target data x as the optimization of another neural network (called the attacker network) to search for the seed to reproduce x. The final reconstruction error is used directly to conclude whether x is in the training data or not. We show through experiments on a variety of data sets and a suite of training parameters that our attacker network can be more successful than prior membership attacks; co-membership attack can be more powerful than single attacks; and VAEs are more susceptible to membership attacks compared to GANs in general. We also discussed membership attack with model generalization, overfitting, and diversity of the model.

Connected through hyperedge HE-1 (induced by source paper "privacy preserving machine learning through data obfuscation"):
  with members:
    - "exploiting unintended feature leakage in collaborative learning"
    - "chiron privacy preserving machine learning as a service"
    - "generating differentially private datasets using gans"
    - "ml leaks model and data independent membership inference attacks and defenses on machine learning models"
    - "understanding membership inferences on well generalized learning models"
    - "stealing hyperparameters in machine learning"
    - "random erasing data augmentation"
    - "logan membership inference attacks against generative models"

Connected through hyperedge HE-2 (induced by source paper "differentially private data generative models"):
  with members:
    - "differentially private mixture of generative neural networks"
    - "knock knock who s there membership inference on aggregate location data"
    - "deep models under the gan information leakage from collaborative deep learning"
    - "learning privately from multiparty data"
    - "differentially private publication of location entropy"
    - "membership inference attacks against machine learning models"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "towards vision based deep reinforcement learning for robotic motion control"

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)