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 "techniques d anonymisation tabulaire concepts et mise en oeuvre"):
  Abstract: In this document, we present a state of the art of anonymization techniques for classical tabular datasets. This article is geared towards a general public having some knowledge of mathematics and computer science, but with no need for specific knowledge in anonymization. The objective of this document it to explain anonymization concepts in order to be able to sanitize a dataset and compute reindentification risk. The document contains a large number of examples to help understand the calculations. #R##N#----- #R##N#Dans ce document, nous presentons l'etat de l'art des techniques d'anonymisation pour des bases de donnees classiques (i.e. des tables), a destination d'un public technique ayant une formation universitaire de base en mathematiques et informatique, mais non specialiste. L'objectif de ce document est d'expliquer les concepts permettant de realiser une anonymisation de donnees tabulaires, et de calculer les risques de reidentification. Le document est largement compose d'exemples permettant au lecteur de comprendre comment mettre en oeuvre les calculs.

Includes members:
  Member "when the signal is in the noise exploiting diffix s sticky noise":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "averaging attacks on bounded noise based disclosure control algorithms")
  Member "differentially private mixture of generative neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "differentially private mixed type data generation for unsupervised learning")
  Member "building a rappor with the unknown privacy preserving learning of associations and data dictionaries":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "wireless federated learning with local differential privacy")
  Member "from t closeness to differential privacy and vice versa in data anonymization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "technical privacy metrics a systematic survey")
  Member "publishing microdata with a robust privacy guarantee":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "the future of statistical disclosure control")

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)