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: "linked data integration with conflicts"
  Abstract: Linked Data have emerged as a successful publication format and one of its main strengths is its fitness for integration of data from multiple sources. This gives them a great potential both for semantic applications and the enterprise environment where data integration is crucial. Linked Data integration poses new challenges, however, and new algorithms and tools covering all steps of the integration process need to be developed. This paper explores Linked Data integration and its specifics. We focus on data fusion and conflict resolution: two novel algorithms for Linked Data fusion with provenance tracking and quality assessment of fused data are proposed. The algorithms are implemented as part of the ODCleanStore framework and evaluated on real Linked Open Data.

Connected through hyperedge HE-1 (induced by source paper "exploiting source object network to resolve object conflicts in linked data"):
  with members:
    - "truthdiscover a demonstration of resolving object conflicts on massive linked data"
    - "truth discovery to resolve object conflicts in linked data"
    - "knowledge based trust estimating the trustworthiness of web sources"
    - "a bayesian approach to discovering truth from conflicting sources for data integration"

Connected through hyperedge HE-2 (induced by source paper "truthdiscover a demonstration of resolving object conflicts on massive linked data"):
  with members:
    - "truth discovery to resolve object conflicts in linked data"

Connected through hyperedge HE-3 (induced by source paper "truth discovery to resolve object conflicts in linked data"):
  with members:
    - "truth finding on the deep web is the problem solved"
    - "a survey on truth discovery"
    - "knowledge based trust estimating the trustworthiness of web sources"
    - "loopy belief propagation for approximate inference an empirical study"
    - "a bayesian approach to discovering truth from conflicting sources for data integration"

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)