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: "t closeness through microaggregation strict privacy with enhanced utility preservation"
  Abstract: Microaggregation is a technique for disclosure limitation aimed at protecting the privacy of data subjects in microdata releases. It has been used as an alternative to generalization and suppression to generate    $k$      -anonymous data sets, where the identity of each subject is hidden within a group of   $k$       subjects. Unlike generalization, microaggregation perturbs the data and this additional masking freedom allows improving data utility in several ways, such as increasing data granularity, reducing the impact of outliers, and avoiding discretization of numerical data.   $k$      -Anonymity, on the other side, does not protect against attribute disclosure, which occurs if the variability of the confidential values in a group of   $k$       subjects is too small. To address this issue, several refinements of   $k$      -anonymity have been proposed, among which   $t$      -closeness stands out as providing one of the strictest privacy guarantees. Existing algorithms to generate    $t$      -close data sets are based on generalization and suppression (they are extensions of    $k$      -anonymization algorithms based on the same principles). This paper proposes and shows how to use microaggregation to generate   $k$      -anonymous    $t$      -close data sets. The advantages of microaggregation are analyzed, and then several microaggregation algorithms for   $k$      -anonymous    $t$      -closeness are presented and empirically evaluated.

Connected through hyperedge HE-1 (induced by source paper "a tutorial on computing t closeness"):
  with members:
    - "on the complexity of t closeness anonymization and related problems"

Connected through hyperedge HE-2 (induced by source paper "improving suppression to reduce disclosure risk and enhance data utility"):
  with members:
    - "a novel microdata privacy disclosure risk measure"

Connected through hyperedge HE-3 (induced by source paper "privacy enhancing aggregation of internet of things data via sensors grouping"):
  with members:
    - "optimization of privacy utility trade offs under informational self determination"
    - "smart meter privacy a utility privacy framework"

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)