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: "provenance views for module privacy"
  Abstract: Scientific workflow systems increasingly store provenance information about the module executions used to produce a data item, as well as the parameter settings and intermediate data items passed between module executions. However, authors/owners of workflows may wish to keep some of this information confidential. In particular, a module may be proprietary, and users should not be able to infer its behavior by seeing mappings between all data inputs and outputs. The problem we address in this paper is the following: Given a workflow, abstractly modeled by a relation R, a privacy requirement \Gamma and costs associated with data. The owner of the workflow decides which data (attributes) to hide, and provides the user with a view R' which is the projection of R over attributes which have not been hidden. The goal is to minimize the cost of hidden data while guaranteeing that individual modules are \Gamma -private. We call this the "secureview" problem. We formally define the problem, study its complexity, and offer algorithmic solutions.

Connected through hyperedge HE-1 (induced by source paper "viewpoint personal data and the internet of things it is time to care about digital provenance"):
  with members:
    - "runtime analysis of whole system provenance"
    - "high throughput ingest of provenance records into accumulo"
    - "provabs model policy and tooling for abstracting prov graphs"

Connected through hyperedge HE-2 (induced by source paper "an analytical survey of provenance sanitization"):
  with members:
    - "a propagation model for provenance views of public private workflows"
    - "surrogate parenthood protected and informative graphs"

Connected through hyperedge HE-3 (induced by source paper "a core calculus for provenance"):
  with members:
    - "surrogate parenthood protected and informative graphs"
    - "causality and the semantics of provenance"
    - "provenance as dependency analysis"
    - "causes and explanations a structural model approach part ii explanations"

Connected through hyperedge HE-4 (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"
    - "transparent anonymization thwarting adversaries who know the algorithm"
    - "differential privacy via wavelet transforms"
    - "composition attacks and auxiliary information in data privacy"

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)