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 "the discretised lognormal and hooked power law distributions for complete citation data best options for modelling and regression"):
  Abstract: Identifying the statistical distribution that best fits citation data is important to allow robust and powerful quantitative analyses. Whilst previous studies have suggested that both the hooked power law and discretised lognormal distributions fit better than the power law and negative binomial distributions, no comparisons so far have covered all articles within a discipline, including those that are uncited. Based on an analysis of 26 different Scopus subject areas in seven different years, this article reports comparisons of the discretised lognormal and the hooked power law with citation data, adding 1 to citation counts in order to include zeros. The hooked power law fits better in two thirds of the subject/year combinations tested for journal articles that are at least three years old, including most medical, life and natural sciences, and for virtually all subject areas for younger articles. Conversely, the discretised lognormal tends to fit best for arts, humanities, social science and engineering fields. The difference between the fits of the distributions is mostly small, however, and so either could reasonably be used for modelling citation data. For regression analyses the best option is to use ordinary least squares regression applied to the natural logarithm of citation counts plus one, especially for sets of younger articles, because of the increased precision of the parameters.

Includes members:
  Member "the precision of the arithmetic mean geometric mean and percentiles for citation data an experimental simulation modelling approach":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "can google scholar and mendeley help to assess the scholarly impacts of dissertations")
  Member "more precise methods for national research citation impact comparisons":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "mendeley reader counts for us computer science conference papers and journal articles")
  Member "national research impact indicators from mendeley readers":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "readership data and research impact")
  Member "regression for citation data an evaluation of different methods":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the citation advantage of linking publications to research data")
  Member "distributions for cited articles from individual subjects and years":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "how much research shared on facebook is hidden from public view a comparison of public and private online activity around plos one papers")
  Member "power laws in citation distributions evidence from scopus":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "the stability of twitter metrics a study on unavailable twitter mentions of scientific publications")
  Member "towards a new crown indicator some theoretical considerations":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "how well does i3 perform for impact measurement compared to other bibliometric indicators the convergent validity of several field normalized indicators")

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)