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 "data and model dependencies of membership inference attack"):
  Abstract: Machine Learning (ML) techniques are used by most data-driven organisations to extract insights. Machine-learning-as-a-service (MLaaS), where models are trained on potentially sensitive user data and then queried by external parties are becoming a reality. However, recently, these systems have been shown to be vulnerable to Membership Inference Attacks (MIA), where a target's data can be inferred to belong or not to the training data. While the key factors for the success of MIA have not been fully understood, existing defence mechanisms only consider the model-specific properties. We investigate the impact of both the data and ML model properties on the vulnerability of ML techniques to MIA. Our analysis indicates a strong relationship between the MIA success and the properties of the data in use, such as the data size and balance between the classes as well as the model properties including the fairness in prediction and the mutual information between the records and the model's parameters. We then propose new approaches to protect ML models from MIA by using several properties, e.g. the model's fairness and mutual information between the records and the model's parameters as regularizers, which reduces the attack accuracy by 25%, while yielding a fairer and a better performing ML model.

Includes members:
  Member "gan leaks a taxonomy of membership inference attacks against gans":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "modelling and quantifying membership information leakage in machine learning")
  Member "stolen memories leveraging model memorization for calibrated white box membership inference":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "modelling and quantifying membership information leakage in machine learning")
  Member "disparate vulnerability on the unfairness of privacy attacks against machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "modelling and quantifying membership information leakage in machine learning")
  Member "membership inference attacks on sequence to sequence models":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "thieves on sesame street model extraction of bert based apis")
  Member "towards demystifying membership inference attacks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "modelling and quantifying membership information leakage in machine learning")
  Member "adversarial attacks against automatic speech recognition systems via psychoacoustic hiding":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "n ml mitigating adversarial examples via ensembles of topologically manipulated classifiers")
  Member "algorithms that remember model inversion attacks and data protection law":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "bias in data driven ai systems an introductory survey")
  Member "ml leaks model and data independent membership inference attacks and defenses on machine learning models":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "dynamic backdoor attacks against machine learning models")

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)