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 "a review of privacy essentials for confidential mobile data transactions"):
  Abstract: The increasingly rapid use of mobile devices for da ta transaction around the world has consequently le d to a new problem, and that is, how to engage in mobile d ata transactions while maintaining an acceptable le vel of data privacy and security. While most mobile devices engage in d ata transactions through a data cloud or a set of d ata servers, it is still possible to apply data confidentiality across data servers, and, as such, preserving privacy in any mo bile data transaction. Yet still, it is essential that a review of data privacy, data utility, the techniques, and methodolog ies employed in the data privacy process, is done, as the underlying data pr ivacy principles remain the same. In this paper, as a contribution, we present a review of data privacy essentials that ar e fundamental in delivering any appropriate analysi s and specific methodology implementation for various data privacy needs in mobile data transactions and computation.

Includes members:
  Member "utilizing noise addition for data privacy an overview":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "data masking with privacy guarantees")
  Member "utility and privacy of data sources can shannon help conceal and reveal information":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "utilizing noise addition for data privacy an overview")
  Member "the hardness and approximation algorithms for l diversity":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "privacy preserving blockchain based federated learning with differential data sharing")
  Member "composition attacks and auxiliary information in data privacy":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "local differential privacy for deep learning")

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)