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 "weight annotation in information extraction"):
  Abstract: The framework of document spanners abstracts the task of information extraction from text as a function that maps every document (a string) into a relation over the document's spans (intervals identified by their start and end indices). For instance, the regular spanners are the closure under the Relational Algebra (RA) of the regular expressions with capture variables, and the expressive power of the regular spanners is precisely captured by the class of vset-automata - a restricted class of transducers that mark the endpoints of selected spans.  In this work, we embark on the investigation of document spanners that can annotate extractions with auxiliary information such as confidence, support, and confidentiality measures. To this end, we adopt the abstraction of provenance semirings by Green et al., where tuples of a relation are annotated with the elements of a commutative semiring, and where the annotation propagates through the (positive) RA operators via the semiring operators. Hence, the proposed spanner extension, referred to as an annotator, maps every string into an annotated relation over the spans. As a specific instantiation, we explore weighted vset-automata that, similarly to weighted automata and transducers, attach semiring elements to transitions. We investigate key aspects of expressiveness, such as the closure under the positive RA, and key aspects of computational complexity, such as the enumeration of annotated answers and their ranked enumeration in the case of numeric semirings. For a number of these problems, fundamental properties of the underlying semiring, such as positivity, are crucial for establishing tractability.

Includes members:
  Member "efficient logspace classes for enumeration counting and uniform generation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "characterizations and approximability of hard counting classes below p")
  Member "complexity bounds for relational algebra over document spanners":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "grammars for document spanenrs")
  Member "representing formal languages a comparison between finite automata and recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "verifiable rnn based policies for pomdps under temporal logic constraints")
  Member "constant delay enumeration for nondeterministic document spanners":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "grammars for document spanenrs")
  Member "constant delay algorithms for regular document spanners":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "grammars for document spanenrs")
  Member "document spanners for extracting incomplete information expressiveness and complexity":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "grammars for document spanenrs")
  Member "snorkel rapid training data creation with weak supervision":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "med7 a transferable clinical natural language processing model for electronic health records")
  Member "extracting automata from recurrent neural networks using queries and counterexamples":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "verifiable rnn based policies for pomdps under temporal logic constraints")

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.DB(Databases)