human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "language integrated provenance"):
  Abstract: Provenance, or information about the origin or derivation of data, is important for assessing the trustworthiness of data and identifying and correcting mistakes. Most prior implementations of data provenance have involved heavyweight modifications to database systems and little attention has been paid to how the provenance data can be used outside such a system. We present extensions to the Links programming language that build on its support for language-integrated query to support provenance queries by rewriting and normalizing monadic comprehensions and extending the type system to distinguish provenance metadata from normal data. The main contribution of this paper is to show that the two most common forms of provenance can be implemented efficiently and used safely as a programming language feature with no changes to the database system.

Includes members:
  Member "database queries that explain their work":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "language integrated provenance by trace analysis")
  Member "effective quotation relating approaches to language integrated query":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "type level computations for ruby libraries")
  Member "first class functions for first order database engines":
    also connected through other hyperedges:
      (none)
  Member "provenance for aggregate queries":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "priu a provenance based approach for incrementally updating regression models")
  Member "provenance as dependency analysis":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the relationship between trust in ai and trustworthy machine learning technologies")

Question: Please tell me which class the source paper 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.PL(Programming Languages)