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: "memguard defending against black box membership inference attacks via adversarial examples"
  Abstract: In a membership inference attack, an attacker aims to infer whether a data sample is in a target classifier's training dataset or not. Specifically, given a black-box access to the target classifier, the attacker trains a binary classifier, which takes a data sample's confidence score vector predicted by the target classifier as an input and predicts the data sample to be a member or non-member of the target classifier's training dataset. Membership inference attacks pose severe privacy and security threats to the training dataset. Most existing defenses leverage differential privacy when training the target classifier or regularize the training process of the target classifier. These defenses suffer from two key limitations: 1) they do not have formal utility-loss guarantees of the confidence score vectors, and 2) they achieve suboptimal privacy-utility tradeoffs. #R##N#In this work, we propose MemGuard, the first defense with formal utility-loss guarantees against black-box membership inference attacks. Instead of tampering the training process of the target classifier, MemGuard adds noise to each confidence score vector predicted by the target classifier. Our key observation is that attacker uses a classifier to predict member or non-member and classifier is vulnerable to adversarial examples. Based on the observation, we propose to add a carefully crafted noise vector to a confidence score vector to turn it into an adversarial example that misleads the attacker's classifier. Our experimental results on three datasets show that MemGuard can effectively defend against membership inference attacks and achieve better privacy-utility tradeoffs than existing defenses. Our work is the first one to show that adversarial examples can be used as defensive mechanisms to defend against membership inference attacks.

Connected through hyperedge HE-1 (induced by source paper "dynamic backdoor attacks against machine learning models"):
  with members:
    - "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"
    - "manipulating machine learning poisoning attacks and countermeasures for regression learning"

Connected through hyperedge HE-2 (induced by source paper "privacy for all demystify vulnerability disparity of differential privacy against membership inference attack"):
  with members:
    - "evaluating differentially private machine learning in practice"
    - "exploiting unintended feature leakage in collaborative learning"
    - "gids gan based intrusion detection system for in vehicle network"
    - "the natural auditor how to tell if someone used your words to train their model"
    - "counterfactual fairness in text classification through robustness"
    - "ml leaks model and data independent membership inference attacks and defenses on machine learning models"
    - "understanding membership inferences on well generalized learning models"
    - "neuzz efficient fuzzing with neural program smoothing"

Connected through hyperedge HE-3 (induced by source paper "membership inference attacks and defenses in supervised learning via generalization gap"):
  with members:
    - "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"
    - "towards measuring membership privacy"
    - "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-4 (induced by source paper "updates leak data set inference and reconstruction attacks in online learning"):
  with members:
    - "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"
    - "cost sensitive robustness against adversarial examples"

Connected through hyperedge HE-5 (induced by source paper "towards plausible graph anonymization"):
  with members:
    - "language in our time an empirical analysis of hashtags"
    - "tagvisor a privacy advisor for sharing hashtags"
    - "privacy in social media identification mitigation and applications"
    - "ml leaks model and data independent membership inference attacks and defenses on machine learning models"
    - "knock knock who s there membership inference on aggregate location data"
    - "walk2friends inferring social links from mobility profiles"
    - "evolution of ego networks in social media with link recommendations"
    - "node2vec scalable feature learning for networks"

Connected through hyperedge HE-6 (induced by source paper "fault tolerance of neural networks in adversarial settings"):
  with members:
    - "evaluating differentially private machine learning in practice"
    - "theoretically principled trade off between robustness and accuracy"
    - "adversarial fault tolerant training for deep neural networks"
    - "privacy risks of explaining machine learning models"
    - "updates leak data set inference and reconstruction attacks in online learning"
    - "ml leaks model and data independent membership inference attacks and defenses on machine learning models"
    - "differentially private fair learning"
    - "renyi differential privacy"

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)