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: "sok differential privacies"
  Abstract: Shortly after its introduction in 2006, differential privacy became the flagship data privacy definition. Since then, numerous variants and extensions were proposed to adapt it to different scenarios and attacker models. In this work, we propose a systematic taxonomy of these variants and extensions. We list all data privacy definitions based on differential privacy, and partition them into seven categories, depending on which aspect of the original definition is modified. These categories act like dimensions: variants belonging to the same category can, in general not be combined, but several categories can be combined to form new definitions. We also establish a partial ordering between variants by summarizing results about their relative strength. Furthermore, we list which of these definitions satisfy some desirable properties, like composition, post-processing, and convexity.

Connected through hyperedge HE-1 (induced by source paper "differentially private sql with bounded user contribution"):
  with members:
    - "detecting violations of differential privacy"
    - "shrinkwrap differentially private query processing in private data federations"
    - "plausible deniability for privacy preserving data synthesis"
    - "renyi differential privacy"
    - "towards practical differential privacy for sql queries"
    - "concentrated differential privacy simplifications extensions and lower bounds"
    - "differential privacy an estimation theory based method for choosing epsilon"
    - "a data and workload aware algorithm for range queries under differential privacy"

Connected through hyperedge HE-2 (induced by source paper "local distribution obfuscation via probability coupling"):
  with members:
    - "local obfuscation mechanisms for hiding probability distributions"
    - "profile based privacy for locally private computations"
    - "utility optimized local differential privacy mechanisms for distribution estimation"
    - "renyi differential privacy"
    - "on the compositionality of quantitative information flow"
    - "differential privacy as a mutual information constraint"
    - "concentrated differential privacy"
    - "rappor randomized aggregatable privacy preserving ordinal response"

Connected through hyperedge HE-3 (induced by source paper "differential privacy techniques for cyber physical systems a survey"):
  with members:
    - "quantifying differential privacy in continuous data release under temporal correlations"
    - "improving the gaussian mechanism for differential privacy analytical calibration and optimal denoising"
    - "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"
    - "achieving differential privacy against non intrusive load monitoring in smart grid a fog computing approach"
    - "toward practical privacy preserving analytics for iot and cloud based healthcare systems"
    - "privacy preserving distributed deep learning for clinical data"
    - "re dpoctor real time health data releasing with w day differential privacy"
    - "charging and discharging of plug in electric vehicles pevs in vehicle to grid v2g systems a cyber insurance based model"

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)