human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "towards demystifying membership inference attacks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "data and model dependencies of membership inference attack")
  Member "the secret sharer measuring unintended neural network memorization extracting secrets":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "membership inference attacks on sequence to sequence models is my data in your machine translation system")
  Member "personalizing dialogue agents i have a dog do you have pets too":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "attention over parameters for dialogue systems")
  Member "understanding membership inferences on well generalized learning models":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "dynamic backdoor attacks against machine learning models")
  Member "extreme adaptation for personalized neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "machine translation system selection from bandit feedback")
  Member "stealing hyperparameters in machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "dynamic backdoor attacks against machine learning models")
  Member "knock knock who s there membership inference on aggregate location data":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "dynamic backdoor attacks against machine learning models")
  Member "differentially private federated learning a client level perspective":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "trends and advancements in deep neural network communication")

Question: Please tell me which class the source paper 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)