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: "putting lipstick on pig enabling database style workflow provenance"
  Abstract: Workflow provenance typically assumes that each module is a "black-box", so that each output depends on all inputs (coarse-grained dependencies). Furthermore, it does not model the internal state of a module, which can change between repeated executions. In practice, however, an output may depend on only a small subset of the inputs (fine-grained dependencies) as well as on the internal state of the module. We present a novel provenance framework that marries database-style and workflow-style provenance, by using Pig Latin to expose the functionality of modules, thus capturing internal state and fine-grained dependencies. A critical ingredient in our solution is the use of a novel form of provenance graph that models module invocations and yields a compact representation of fine-grained workflow provenance. It also enables a number of novel graph transformation operations, allowing to choose the desired level of granularity in provenance querying (ZoomIn and ZoomOut), and supporting "what-if" workflow analytic queries. We implemented our approach in the Lipstick system and developed a benchmark in support of a systematic performance evaluation. Our results demonstrate the feasibility of tracking and querying fine-grained workflow provenance.

Connected through hyperedge HE-1 (induced by source paper "priu a provenance based approach for incrementally updating regression models"):
  with members:
    - "holodetect few shot learning for error detection"
    - "manipulating and measuring model interpretability"
    - "boostclean automated error detection and repair for machine learning"
    - "the mythos of model interpretability"
    - "processing analytical workloads incrementally"
    - "linview incremental view maintenance for complex analytical queries"
    - "provenance for aggregate queries"

Connected through hyperedge HE-2 (induced by source paper "qfix diagnosing errors through query histories"):
  with members:
    - "clamshell speeding up crowds for low latency data labeling"
    - "high level why not explanations using ontologies"
    - "perfxplain debugging mapreduce job performance"
    - "a bayesian approach to discovering truth from conflicting sources for data integration"
    - "explanation based auditing"

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"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "reenactment for read committed snapshot isolation"):
  with members:
    - "first order provenance games"
    - "provenance for aggregate queries"
    - "a critique of ansi sql isolation levels"

Connected through hyperedge HE-5 (induced by source paper "database queries that explain their work"):
  with members:
    - "a core calculus for provenance"
    - "provenance for aggregate queries"
    - "provenance as dependency analysis"

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)