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 and data differencing for workflow reproducibility analysis"
  Abstract: One of the foundations of science is that researchers must publish the methodology used to achieve their results so that others can attempt to reproduce them. This has the added benefit of allowing methods to be adopted and adapted for other purposes. In the field of e-Science, services -- often choreographed through workflow, process data to generate results. The reproduction of results is often not straightforward as the computational objects may not be made available or may have been updated since the results were generated. For example, services are often updated to fix bugs or improve algorithms. This paper addresses these problems in three ways. Firstly, it introduces a new framework to clarify the range of meanings of "reproducibility". Secondly, it describes a new algorithm, \PDIFF, that uses a comparison of workflow provenance traces to determine whether an experiment has been reproduced; the main innovation is that if this is not the case then the specific point(s) of divergence are identified through graph analysis, assisting any researcher wishing to understand those differences. One key feature is support for user-defined, semantic data comparison operators. Finally, the paper describes an implementation of \PDIFF that leverages the power of the e-Science Central platform which enacts workflows in the cloud. As well as automatically generating a provenance trace for consumption by \PDIFF, the platform supports the storage and re-use of old versions of workflows, data and services; the paper shows how this can be powerfully exploited in order to achieve reproduction and re-use.

Connected through hyperedge HE-1 (induced by source paper "cloud infrastructure provenance collection and management to reproduce scientific workflows execution"):
  with members:
    - "development of a large scale neuroimages and clinical variables data atlas in the neugrid4you n4u project"
    - "scientific workflow repeatability through cloud aware provenance"
    - "cloud computing and grid computing 360 degree compared"

Connected through hyperedge HE-2 (induced by source paper "the lifecycle of provenance metadata and its associated challenges and opportunities"):
  with members:
    - "provabs model policy and tooling for abstracting prov graphs"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "using cloud aware provenance to reproduce scientific workflow execution on cloud"):
  with members:
    - "an integrated e science analysis base for computation neuroscience experiments and analysis"
    - "cloud computing and grid computing 360 degree compared"

Connected through hyperedge HE-4 (induced by source paper "scientific workflow repeatability through cloud aware provenance"):
  with members:
    - "an integrated e science analysis base for computation neuroscience experiments and analysis"
    - "cloud computing and grid computing 360 degree compared"

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)