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: "towards demystifying membership inference attacks"
  Abstract: Membership inference attacks seek to infer membership of individual training instances of a model to which an adversary has black-box access through a machine learning-as-a-service API. Aiming at providing an in-depth characterization of membership privacy risks against machine learning models, this paper presents a comprehensive study towards demystifying membership inference attacks from two complimentary perspectives. First, we provide a generalized formulation of the development of a black-box membership inference attack model. Second, we characterize the importance of model choice on model vulnerability through a systematic evaluation of a variety of machine learning models and model combinations using multiple datasets. Through formal analysis and empirical evidence from extensive experimentation, we characterize under what conditions a model may be vulnerable to such black-box membership inference attacks. We show that membership inference vulnerability is data-driven and its attack models are largely transferable. Though different model types display different vulnerabilities to membership inferences, so do different datasets. Our empirical results additionally show that (1) using the type of target model under attack within the attack model may not increase attack effectiveness and (2) collaborative learning in federated systems exposes vulnerabilities to membership inference risks when the adversary is a participant in the federation. We also discuss countermeasure and mitigation strategies.

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"
    - "disparate vulnerability on the unfairness of privacy attacks against machine learning"
    - "membership inference attacks on sequence to sequence models"
    - "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"
    - "disparate vulnerability on the unfairness of privacy attacks against machine learning"
    - "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 "does learning require memorization a short tale about a long tail"):
  with members:
    - "learning imbalanced datasets with label distribution aware margin loss"
    - "two models of double descent for weak features"
    - "harmless interpolation of noisy data in regression"
    - "high probability generalization bounds for uniformly stable algorithms with nearly optimal rate"
    - "large scale fine grained categorization and domain specific transfer learning"
    - "dnn or k nn that is the generalize vs memorize question"
    - "privacy preserving prediction"
    - "understanding membership inferences on well generalized learning models"

Connected through hyperedge HE-4 (induced by source paper "auditing data provenance in text generation models"):
  with members:
    - "the secret sharer measuring unintended neural network memorization extracting secrets"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "understanding membership inferences on well generalized learning models"
    - "extreme adaptation for personalized neural machine translation"
    - "knock knock who s there membership inference on aggregate location data"
    - "logan membership inference attacks against generative models"
    - "privacy risk in machine learning analyzing the connection to overfitting"
    - "machine learning models that remember too much"

Connected through hyperedge HE-5 (induced by source paper "pac learning with stable and private predictions"):
  with members:
    - "high probability generalization bounds for uniformly stable algorithms with nearly optimal rate"
    - "privately answering classification queries in the agnostic pac model"
    - "model agnostic private learning via stability"
    - "private pac learning implies finite littlestone dimension"
    - "privacy preserving prediction"
    - "towards measuring membership privacy"
    - "subgaussian tail bounds via stability arguments"
    - "learning privately from multiparty data"

Connected through hyperedge HE-6 (induced by source paper "privacy attacks on network embeddings"):
  with members:
    - "graph embedding techniques applications and performance a survey"
    - "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"
    - "sok security and privacy in machine learning"
    - "a comprehensive survey of graph embedding problems techniques and applications"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"
    - "learning convolutional neural networks for graphs"

Connected through hyperedge HE-7 (induced by source paper "membership inference attacks on sequence to sequence models"):
  with members:
    - "on the impact of various types of noise on neural machine translation"
    - "algorithms that remember model inversion attacks and data protection law"
    - "a call for clarity in reporting bleu scores"
    - "ml leaks model and data independent membership inference attacks and defenses on machine learning models"
    - "understanding membership inferences on well generalized learning models"
    - "knock knock who s there membership inference on aggregate location data"
    - "sockeye a toolkit for neural machine translation"
    - "attention is all you need"

Connected through hyperedge HE-8 (induced by source paper "privacy preserving gradient boosting decision trees"):
  with members:
    - "a survey on federated learning systems vision hype and reality for data privacy and protection"
    - "practical federated gradient boosting decision trees"
    - "multi layered gradient boosting decision trees"
    - "catboost unbiased boosting with categorical features"
    - "differentially private mixture of generative neural networks"
    - "xgboost a scalable tree boosting system"
    - "membership inference attacks against machine learning models"
    - "scikit learn machine learning in python"

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.CR(Cryptography and Security)