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 "improving the representation and conversion of mathematical formulae by considering their textual context"):
  Abstract: Mathematical formulae represent complex semantic information in a concise form. Especially in Science, Technology, Engineering, and Mathematics, mathematical formulae are crucial to communicate information, e.g., in scientific papers, and to perform computations using computer algebra systems. Enabling computers to access the information encoded in mathematical formulae requires machine-readable formats that can represent both the presentation and content, i.e., the semantics, of formulae. Exchanging such information between systems additionally requires conversion methods for mathematical representation formats. We analyze how the semantic enrichment of formulae improves the format conversion process and show that considering the textual context of formulae reduces the error rate of such conversions. Our main contributions are: (1) providing an openly available benchmark dataset for the mathematical format conversion task consisting of a newly created test collection, an extensive, manually curated gold standard and task-specific evaluation metrics; (2) performing a quantitative evaluation of state-of-the-art tools for mathematical format conversions; (3) presenting a new approach that considers the textual context of formulae to reduce the error rate for mathematical format conversions. Our benchmark dataset facilitates future research on mathematical format conversions as well as research on many problems in mathematical information retrieval. Because we annotated and linked all components of formulae, e.g., identifiers, operators and other entities, to Wikidata entries, the gold standard can, for instance, be used to train methods for formula concept discovery and recognition. Such methods can then be applied to improve mathematical information retrieval systems, e.g., for semantic formula search, recommendation of mathematical content, or detection of mathematical plagiarism.

Includes members:
  Member "vmext a visualization tool for mathematical expression trees":
    also connected through other hyperedges:
      (none)
  Member "interoperability in the opendreamkit project the math in the middle approach":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "ontomath digital ecosystem ontologies mathematical knowledge analytics and management")
  Member "growing the digital repository of mathematical formulae with generic latex sources":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "vmext a visualization tool for mathematical expression trees")
  Member "digital repository of mathematical formulae":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "fungrim a symbolic library for special functions")
  Member "mathoid robust scalable fast and accessible math rendering for wikipedia":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "vmext a visualization tool for mathematical expression trees")
  Member "branch cuts in maple 17":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "semantic preserving bijective mappings for expressions involving special functions between computer algebra systems and document preparation systems")
  Member "rted a robust algorithm for the tree edit distance":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "fixminer mining relevant fix patterns for automated program repair")

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.DL(Digital Libraries)