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: "attriguard a practical defense against attribute inference attacks via adversarial machine learning"
  Abstract: Users in various web and mobile applications are vulnerable to attribute inference attacks, in which an attacker leverages a machine learning classifier to infer a target user's private attributes (e.g., location, sexual orientation, political view) from its public data (e.g., rating scores, page likes). Existing defenses leverage game theory or heuristics based on correlations between the public data and attributes. These defenses are not practical. Specifically, game-theoretic defenses require solving intractable optimization problems, while correlation-based defenses incur large utility loss of users' public data. #R##N#In this paper, we present AttriGuard, a practical defense against attribute inference attacks. AttriGuard is computationally tractable and has small utility loss. Our AttriGuard works in two phases. Suppose we aim to protect a user's private attribute. In Phase I, for each value of the attribute, we find a minimum noise such that if we add the noise to the user's public data, then the attacker's classifier is very likely to infer the attribute value for the user. We find the minimum noise via adapting existing evasion attacks in adversarial machine learning. In Phase II, we sample one attribute value according to a certain probability distribution and add the corresponding noise found in Phase I to the user's public data. We formulate finding the probability distribution as solving a constrained convex optimization problem. We extensively evaluate AttriGuard and compare it with existing methods using a real-world dataset. Our results show that AttriGuard substantially outperforms existing methods. Our work is the first one that shows evasion attacks can be used as defensive techniques for privacy protection.

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 "everything about you a multimodal approach towards friendship inference in online social networks"):
  with members:
    - "language in our time an empirical analysis of hashtags"
    - "tagvisor a privacy advisor for sharing hashtags"
    - "you are your metadata identification and obfuscation of social media users using metadata information"
    - "graph embedding techniques applications and performance a survey"
    - "a hybrid model for identity obfuscation by face replacement"
    - "friendships rivalries and trysts characterizing relations between ideas in texts"
    - "connecting pixels to privacy and utility automatic redaction of private information in images"
    - "walk2friends inferring social links from mobility profiles"

Connected through hyperedge HE-3 (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"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "privacy aware recommendation with private attribute protection using adversarial learning"):
  with members:
    - "less is more semi supervised causal inference for detecting pathogenic users in social media"
    - "i am not what i write privacy preserving text representation learning"
    - "hawkes process for understanding the influence of pathogenic social media accounts"
    - "detection of violent extremists in social media"
    - "privacy in social media identification mitigation and applications"
    - "similar but different exploiting users congruity for recommendation systems"
    - "protecting user privacy an approach for untraceable web browsing history and unambiguous user profiles"
    - "early identification of pathogenic social media accounts"

Connected through hyperedge HE-5 (induced by source paper "optimizing privacy preserving outsourced convolutional neural network predictions"):
  with members:
    - "xonn xnor based oblivious deep neural network inference"
    - "chiron privacy preserving machine learning as a service"
    - "adversarial examples that fool both computer vision and time limited humans"
    - "turning your weakness into a strength watermarking deep neural networks by backdooring"
    - "ml leaks model and data independent membership inference attacks and defenses on machine learning models"
    - "gazelle a low latency framework for secure neural network inference"
    - "logan membership inference attacks against generative models"
    - "deepsecure scalable provably secure deep learning"

Connected through hyperedge HE-6 (induced by source paper "defending against machine learning based inference attacks via adversarial examples opportunities and challenges"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "misleading authorship attribution of source code using adversarial learning"
    - "mockingbird defending against deep learning based website fingerprinting attacks with adversarial traces"
    - "tagvisor a privacy advisor for sharing hashtags"
    - "generative adversarial privacy"
    - "poisoning attacks to graph based recommender systems"
    - "graph based security and privacy analytics via collective classification with joint weight learning and propagation"
    - "adversarial binaries for authorship identification"

Connected through hyperedge HE-7 (induced by source paper "certified robustness for top k predictions against adversarial perturbations via randomized smoothing"):
  with members:
    - "tight certificates of adversarial robustness for randomly smoothed classifiers"
    - "certified adversarial robustness via randomized smoothing"
    - "theoretical evidence for adversarial robustness through randomization"
    - "adv bnn improved adversarial defense through robust bayesian neural network"
    - "scaling provable adversarial defenses"
    - "on the effectiveness of interval bound propagation for training verifiably robust models"
    - "sorting out lipschitz function approximation"
    - "a dual approach to scalable verification of deep networks"

Connected through hyperedge HE-8 (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"

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)