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: "disparate vulnerability on the unfairness of privacy attacks against machine learning"
  Abstract: A membership inference attack (MIA) against a machine learning model enables an attacker to determine whether a given data record was part of the model's training dataset or not. Such attacks have been shown to be practical both in centralized and federated settings, and pose a threat in many privacy-sensitive domains such as medicine or law enforcement. In the literature, the effectiveness of these attacks is invariably reported using metrics computed across the whole population. In this paper, we take a closer look at the attack's performance across different subgroups present in the data distributions. We introduce a framework that enables us to efficiently analyze the vulnerability of machine learning models to MIA. We discover that even if the accuracy of MIA looks no better than random guessing over the whole population, subgroups are subject to disparate vulnerability, i.e., certain subgroups can be significantly more vulnerable than others. We provide a theoretical definition for MIA vulnerability which we validate empirically both on synthetic and real data.

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"
    - "stolen memories leveraging model memorization for calibrated white box membership inference"
    - "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"
    - "stolen memories leveraging model memorization for calibrated white box membership inference"
    - "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 "updates leak data set inference and reconstruction attacks in online learning"):
  with members:
    - "memguard defending against black box membership inference attacks via adversarial examples"
    - "diversity sensitive conditional generative adversarial networks"
    - "under the hood of membership inference attacks on aggregate location time series"
    - "how to prove your model belongs to you a blind watermark based framework to protect intellectual property of dnn"
    - "exploiting unintended feature leakage in collaborative learning"
    - "knockoff nets stealing functionality of black box models"
    - "tagvisor a privacy advisor for sharing hashtags"
    - "cost sensitive robustness against adversarial examples"

Connected through hyperedge HE-4 (induced by source paper "reconciling utility and membership privacy via knowledge distillation"):
  with members:
    - "stolen memories leveraging model memorization for calibrated white box membership inference"
    - "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-5 (induced by source paper "differential privacy has disparate impact on model accuracy"):
  with members:
    - "diversity in faces"
    - "fair decision making using privacy protected data"
    - "class balanced loss based on effective number of samples"
    - "agnostic federated learning"
    - "federated heavy hitters discovery with differential privacy"
    - "a general approach to adding differential privacy to iterative training procedures"
    - "adversarial removal of demographic attributes from text data"
    - "differentially private fair learning"

Connected through hyperedge HE-6 (induced by source paper "on inferring training data attributes in machine learning models"):
  with members:
    - "ml leaks model and data independent membership inference attacks and defenses on machine learning models"
    - "understanding membership inferences on well generalized learning models"
    - "privacy risk in machine learning analyzing the connection to overfitting"
    - "stealing machine learning models via prediction apis"
    - "membership inference attacks against machine learning models"

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)