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: "stolen memories leveraging model memorization for calibrated white box membership inference"
  Abstract: Membership inference (MI) attacks exploit a learned model's lack of generalization to infer whether a given sample was in the model's training set. Known MI attacks generally work by casting the attacker's goal as a supervised learning problem, training an attack model from predictions generated by the target model, or by others like it. However, we find that these attacks do not often provide a meaningful basis for confidently inferring training set membership, as the attack models are not well-calibrated. Moreover, these attacks do not significantly outperform a trivial attack that predicts that a point is a member if and only if the model correctly predicts its label. In this work we present well-calibrated MI attacks that allow the attacker to accurately control the minimum confidence with which positive membership inferences are made. Our attacks take advantage of white-box information about the target model and leverage new insights about how overfitting occurs in deep neural networks; namely, we show how a model's idiosyncratic use of features can provide evidence for membership. Experiments on seven real-world datasets show that our attacks support calibration for high-confidence inferences, while outperforming previous MI attacks in terms of accuracy. Finally, we show that our attacks achieve non-trivial advantage on some models with low generalization error, including those trained with small-epsilon-differential privacy; for large-epsilon (epsilon=16, as reported in some industrial settings), the attack performs comparably to unprotected models.

Connected through hyperedge HE-1 (induced by source paper "data and model dependencies of membership inference attack"):
  with members:
    - "gan leaks a taxonomy of membership inference attacks against gans"
    - "disparate vulnerability on the unfairness of privacy attacks against machine learning"
    - "membership inference attacks on sequence to sequence models"
    - "towards demystifying membership inference attacks"
    - "adversarial attacks against automatic speech recognition systems via psychoacoustic hiding"
    - "algorithms that remember model inversion attacks and data protection law"
    - "ml leaks model and data independent membership inference attacks and defenses on machine learning models"
    - "understanding membership inferences on well generalized learning models"

Connected through hyperedge HE-2 (induced by source paper "modelling and quantifying membership information leakage in machine learning"):
  with members:
    - "gan leaks a taxonomy of membership inference attacks against gans"
    - "on inferring training data attributes in machine learning models"
    - "disparate vulnerability on the unfairness of privacy attacks against machine learning"
    - "towards demystifying membership inference attacks"
    - "ml leaks model and data independent membership inference attacks and defenses on machine learning models"
    - "logan membership inference attacks against generative models"
    - "privacy risk in machine learning analyzing the connection to overfitting"
    - "membership inference attacks against machine learning models"

Connected through hyperedge HE-3 (induced by source paper "reconciling utility and membership privacy via knowledge distillation"):
  with members:
    - "disparate vulnerability on the unfairness of privacy attacks against machine learning"
    - "evaluating differentially private machine learning in practice"
    - "zero shot knowledge distillation in deep networks"
    - "exploiting unintended feature leakage in collaborative learning"
    - "private model compression via knowledge distillation"
    - "the secret sharer measuring unintended neural network memorization extracting secrets"
    - "i know what you see power side channel attack on convolutional neural network accelerators"
    - "ml leaks model and data independent membership inference attacks and defenses on machine learning models"

Connected through hyperedge HE-4 (induced by source paper "characterizing membership privacy in stochastic gradient langevin dynamics"):
  with members:
    - "generalization in generative adversarial networks a novel perspective from privacy protection"
    - "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)