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: "transparent anonymization thwarting adversaries who know the algorithm"
  Abstract: Numerous generalization techniques have been proposed for privacy preserving data publishing. Most existing techniques, however, implicitly assume that the adversary knows little about the anonymization algorithm adopted by the data publisher. Consequently, they cannot guard against privacy attacks that exploit various characteristics of the anonymization mechanism. This paper provides a practical solution to the above problem. First, we propose an analytical model for evaluating disclosure risks, when an adversary knows everything in the anonymization process, except the sensitive values. Based on this model, we develop a privacy principle, transparent l-diversity, which ensures privacy protection against such powerful adversaries. We identify three algorithms that achieve transparent l-diversity, and verify their effectiveness and efficiency through extensive experiments with real data.

Connected through hyperedge HE-1 (induced by source paper "a propagation model for provenance views of public private workflows"):
  with members:
    - "calibrating data to sensitivity in private data analysis"
    - "differentially private publication of sparse data"
    - "surrogate parenthood protected and informative graphs"
    - "provenance views for module privacy"
    - "differential privacy via wavelet transforms"
    - "composition attacks and auxiliary information in data privacy"

Connected through hyperedge HE-2 (induced by source paper "publishing microdata with a robust privacy guarantee"):
  with members:
    - "composition attacks and auxiliary information in data privacy"
    - "a framework for high accuracy privacy preserving mining"

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.DB(Databases)