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: "auditing data provenance in text generation models"
  Abstract: To help enforce data-protection regulations such as GDPR and detect unauthorized uses of personal data, we develop a new \emph{model auditing} technique that helps users 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 popular online services and are often trained on personal data such as users' messages, searches, chats, and comments.  We design and evaluate a 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). We empirically show that our method can successfully audit well-generalized models that are 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 "towards probabilistic verification of machine unlearning"):
  with members:
    - "formalizing data deletion in the context of the right to be forgotten"
    - "nnoculation broad spectrum and targeted treatment of backdoored dnns"
    - "machine unlearning linear filtration for logit based classifiers"
    - "bypassing backdoor detection algorithms in deep learning"
    - "hidden trigger backdoor attacks"
    - "making ai forget you data deletion in machine learning"
    - "certified data removal from machine learning models"
    - "detecting backdoor attacks on deep neural networks by activation clustering"

Connected through hyperedge HE-2 (induced by source paper "generative models for effective ml on private decentralized datasets"):
  with members:
    - "dp cgan differentially private synthetic data and label generation"
    - "taxonomy of real faults in deep learning systems"
    - "reconstruction and membership inference attacks against generative models"
    - "towards federated learning at scale system design"
    - "differentially private generative adversarial networks for time series continuous and discrete open data"
    - "evaluating differentially private machine learning in practice"
    - "videoflow a flow based generative model for video"
    - "detecting bias with generative counterfactual face attribute augmentation"

Connected through hyperedge HE-3 (induced by source paper "thieves on sesame street model extraction of bert based apis"):
  with members:
    - "boolq exploring the surprising difficulty of natural yes no questions"
    - "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"
    - "high fidelity extraction of neural network models"
    - "zero shot knowledge transfer via adversarial belief matching"
    - "learning and evaluating general linguistic intelligence"
    - "a framework for the extraction of deep neural networks by leveraging public data"
    - "membership inference attacks on sequence to sequence models"
    - "prediction poisoning utility constrained defenses against model stealing attacks"

Connected through hyperedge HE-4 (induced by source paper "privacy and utility preserving textual analysis via calibrated multivariate perturbations"):
  with members:
    - "the natural auditor how to tell if someone used your words to train their model"
    - "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-5 (induced by source paper "analyzing privacy loss in updates of natural language models"):
  with members:
    - "making ai forget you data deletion in machine learning"
    - "updates leak data set inference and reconstruction attacks in online learning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "ml leaks model and data independent membership inference attacks and defenses on machine learning models"
    - "attention is all you need"
    - "learning differentially private recurrent language models"
    - "deep models under the gan information leakage from collaborative deep learning"
    - "membership inference attacks against machine learning 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)