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: "provabs model policy and tooling for abstracting prov graphs"
  Abstract: Provenance metadata can be valuable in data sharing settings, where it can be used to help data consumers form judgements regarding the reliability of the data produced by third parties. However, some parts of provenance may be sensitive, requiring access control, or they may need to be simplified for the intended audience. Both these issues can be addressed by a single mechanism for creating abstractions over provenance, coupled with a policy model to drive the abstraction. Such mechanism, which we refer to as abstraction by grouping, simultaneously achieves partial disclosure of provenance, and facilitates its consumption. In this paper we introduce a formal foundation for this type of abstraction, grounded in the W3C PROV model; describe the associated policy model; and briefly present its implementation, the Provabs tool for interactive experimentation with policies and abstractions.

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"
    - "provenance views for module privacy"

Connected through hyperedge HE-2 (induced by source paper "understanding data science lifecycle provenance via graph segmentation and summarization"):
  with members:
    - "consistently faster and smaller compressed bitmaps with roaring"
    - "foundations of modern query languages for graph databases"
    - "keystoneml optimizing pipelines for large scale advanced analytics"
    - "context free path queries on rdf graphs"
    - "aggregation by provenance types a technique for summarising provenance graphs"
    - "openml networked science in machine learning"

Connected through hyperedge HE-3 (induced by source paper "the lifecycle of provenance metadata and its associated challenges and opportunities"):
  with members:
    - "provenance and data differencing for workflow reproducibility analysis"
    - "provgen generating synthetic prov graphs with predictable structure"
    - "transitive credit as a means to address social and technological concerns stemming from citation and attribution of digital products"
    - "putting lipstick on pig enabling database style workflow provenance"

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)