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: "the natural auditor how to tell if someone used your words to train their model"
  Abstract: To help enforce data-protection regulations such as GDPR and detect unauthorized uses of personal data, we propose a new \emph{model auditing} technique that enables users to check if their data was used to train a machine learning model. We focus on auditing deep-learning models that generate natural-language text, including word prediction and dialog generation. These models are at the core of many popular online services. Furthermore, they are often trained on very sensitive personal data, such as users' messages, searches, chats, and comments. #R##N#We design and evaluate an effective black-box auditing method that can detect, with very few queries to a model, if a particular user's texts were used to train it (among thousands of other users). In contrast to prior work on membership inference against ML models, we do not assume that the model produces numeric confidence values. We empirically demonstrate that we can successfully audit models that are well-generalized and not overfitted to the training data. We also analyze how text-generation models memorize word sequences and explain why this memorization makes them amenable to auditing.

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"
    - "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:
    - "memguard defending against black box membership inference attacks via adversarial examples"
    - "evaluating differentially private machine learning in practice"
    - "exploiting unintended feature leakage in collaborative learning"
    - "gids gan based intrusion detection system for in vehicle network"
    - "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 "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-4 (induced by source paper "leveraging hierarchical representations for preserving privacy and utility in text"):
  with members:
    - "poincare wasserstein autoencoder"
    - "inferring concept hierarchies from text corpora via hyperbolic embeddings"
    - "privacy preserving active learning on sensitive data for user intent classification"
    - "generalised differential privacy for text document processing"
    - "learning continuous hierarchies in the lorentz model of hyperbolic geometry"
    - "poincar e glove hyperbolic word embeddings"
    - "technical privacy metrics a systematic survey"
    - "privacy preserving neural representations of text"

Connected through hyperedge HE-5 (induced by source paper "privacy and utility preserving textual analysis via calibrated multivariate perturbations"):
  with members:
    - "auditing data provenance in text generation models"
    - "technical privacy metrics a systematic survey"
    - "syntf synthetic and differentially private term frequency vectors for privacy preserving text mining"
    - "plausible deniability for privacy preserving data synthesis"
    - "enriching word vectors with subword information"
    - "bolt on differential privacy for scalable stochastic gradient descent based analytics"
    - "membership inference attacks against machine learning models"
    - "applying deep learning to answer selection a study and an open task"

Connected through hyperedge HE-6 (induced by source paper "alleviating privacy attacks via causal learning"):
  with members:
    - "a meta transfer objective for learning to disentangle causal mechanisms"
    - "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"
    - "logan membership inference attacks against generative models"
    - "privacy risk in machine learning analyzing the connection to overfitting"
    - "learning privately from multiparty data"
    - "membership inference attacks against machine learning models"
    - "playing atari with deep reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "the audio auditor participant level membership inference in internet of things voice services"):
  with members:
    - "adversarial attacks against automatic speech recognition systems via psychoacoustic hiding"
    - "stealing hyperparameters in machine learning"
    - "multi task label embedding for text classification"
    - "stealing machine learning models via prediction apis"
    - "membership inference attacks against machine learning models"
    - "deep speech scaling up end to end speech recognition"

Connected through hyperedge HE-8 (induced by source paper "the secret sharer measuring unintended neural network memorization extracting secrets"):
  with members:
    - "evaluating differentially private machine learning in practice"
    - "gmail smart compose real time assisted writing"
    - "towards demystifying membership inference attacks"
    - "an analysis of neural language modeling at multiple scales"
    - "learning to generate reviews and discovering sentiment"
    - "stealing hyperparameters in machine learning"
    - "scaling sgd batch size to 32k for imagenet training"
    - "regularizing and optimizing lstm language models"

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)