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: "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.

Connected through hyperedge HE-1 (induced by source paper "language integrated provenance by trace analysis"):
  with members:
    - "language integrated provenance in haskell"
    - "you say what i hear where and why mis interpreting sql to derive fine grained provenance"
    - "imperative functional programs that explain their work"
    - "database queries that explain their work"
    - "a core calculus for provenance"

Connected through hyperedge HE-2 (induced by source paper "pug a framework and practical implementation for why why not provenance extended version"):
  with members:
    - "semiring provenance for first order model checking"
    - "the complexity of causality and responsibility for query answers and non answers"

Connected through hyperedge HE-3 (induced by source paper "a monadic framework for relational verification applied to information security program equivalence and optimizations"):
  with members:
    - "proof relevant logical relations for name generation"
    - "hypercollecting semantics and its application to static analysis of information flow"
    - "lightdp towards automating differential privacy proofs"
    - "higher order approximate relational refinement types for mechanism design and differential privacy"
    - "the foundational cryptography framework"
    - "abstract effects and proof relevant logical relations"
    - "quantitative information flow as safety and liveness hyperproperties"
    - "logical step indexed logical relations"

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.PL(Programming Languages)