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 measuring membership privacy"
  Abstract: Machine learning models are increasingly made available to the masses through public query interfaces. Recent academic work has demonstrated that malicious users who can query such models are able to infer sensitive information about records within the training data. Differential privacy can thwart such attacks, but not all models can be readily trained to achieve this guarantee or to achieve it with acceptable utility loss. As a result, if a model is trained without differential privacy guarantee, little is known or can be said about the privacy risk of releasing it. In this work, we investigate and analyze membership attacks to understand why and how they succeed. Based on this understanding, we propose Differential Training Privacy (DTP), an empirical metric to estimate the privacy risk of publishing a classier when methods such as differential privacy cannot be applied. DTP is a measure of a classier with respect to its training dataset, and we show that calculating DTP is efficient in many practical cases. We empirically validate DTP using state-of-the-art machine learning models such as neural networks trained on real-world datasets. Our results show that DTP is highly predictive of the success of membership attacks and therefore reducing DTP also reduces the privacy risk. We advocate for DTP to be used as part of the decision-making process when considering publishing a classifier. To this end, we also suggest adopting the DTP-1 hypothesis: if a classifier has a DTP value above 1, it should not be published.

Connected through hyperedge HE-1 (induced by source paper "dynamic backdoor attacks against machine learning models"):
  with members:
    - "memguard defending against black box membership inference attacks via adversarial examples"
    - "strip a defence against trojan attacks on deep neural networks"
    - "under the hood of membership inference attacks on aggregate location time series"
    - "updates leak data set inference and reconstruction attacks in online learning"
    - "exploiting unintended feature leakage in collaborative learning"
    - "knockoff nets stealing functionality of black box models"
    - "tagvisor a privacy advisor for sharing hashtags"
    - "the natural auditor how to tell if someone used your words to train their model"

Connected through hyperedge HE-2 (induced by source paper "membership inference attacks and defenses in supervised learning via generalization gap"):
  with members:
    - "memguard defending against black box membership inference attacks via adversarial examples"
    - "the secret sharer measuring unintended neural network memorization extracting secrets"
    - "ml leaks model and data independent membership inference attacks and defenses on machine learning models"
    - "understanding membership inferences on well generalized learning models"
    - "logan membership inference attacks against generative models"
    - "mixup beyond empirical risk minimization"
    - "privacy risk in machine learning analyzing the connection to overfitting"
    - "deep transfer learning with joint adaptation networks"

Connected through hyperedge HE-3 (induced by source paper "memguard defending against black box membership inference attacks via adversarial examples"):
  with members:
    - "defending against machine learning based inference attacks via adversarial examples opportunities and challenges"
    - "differentially private model publishing for deep learning"
    - "misleading authorship attribution of source code using adversarial learning"
    - "evaluating differentially private machine learning in practice"
    - "under the hood of membership inference attacks on aggregate location time series"
    - "updates leak data set inference and reconstruction attacks in online learning"
    - "exploiting unintended feature leakage in collaborative learning"
    - "tagvisor a privacy advisor for sharing hashtags"

Connected through hyperedge HE-4 (induced by source paper "evaluating differentially private machine learning in practice"):
  with members:
    - "differentially private model publishing for deep learning"
    - "privacy preserving collaborative deep learning with irregular participants"
    - "cache telepathy leveraging shared resource attacks to learn dnn architectures"
    - "differentially private empirical risk minimization revisited faster and more general"
    - "dp admm admm based distributed learning with differential privacy"
    - "how to backdoor federated learning"
    - "ml leaks model and data independent membership inference attacks and defenses on machine learning models"
    - "efficient deep learning on multi source private data"

Connected through hyperedge HE-5 (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"
    - "disparate vulnerability on the unfairness of privacy attacks against machine learning"
    - "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"

Connected through hyperedge HE-6 (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"
    - "towards demystifying membership inference attacks"
    - "dnn or k nn that is the generalize vs memorize question"
    - "privacy preserving prediction"

Connected through hyperedge HE-7 (induced by source paper "gamin an adversarial approach to black box model inversion"):
  with members:
    - "high fidelity extraction of neural network models"
    - "evaluating differentially private machine learning in practice"
    - "mixmatch a holistic approach to semi supervised learning"
    - "exploiting unintended feature leakage in collaborative learning"
    - "csi neural network using side channels to recover your artificial neural network information"
    - "sok security and privacy in machine learning"
    - "stealing hyperparameters in machine learning"
    - "began boundary equilibrium generative adversarial networks"

Connected through hyperedge HE-8 (induced by source paper "towards automatic generation of shareable synthetic clinical notes using neural language models"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "deep ehr chronic disease prediction using medical notes"
    - "towards the creation of a large corpus of synthetically identified clinical notes"
    - "fine tuned language models for text classification"
    - "lessons from natural language inference in the clinical domain"
    - "generating multi label discrete patient records using generative adversarial networks"
    - "privacy risk in machine learning analyzing the connection to overfitting"
    - "de identification of patient notes with recurrent neural 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.CR(Cryptography and Security)