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 "are the discretised lognormal and hooked power law distributions plausible for citation data"):
  Abstract: There is no agreement over which statistical distribution is most appropriate for modelling citation count data. This is important because if one distribution is accepted then the relative merits of different citation-based indicators, such as percentiles, arithmetic means and geometric means, can be more fully assessed. In response, this article investigates the plausibility of the discretised lognormal and hooked power law distributions for modelling the full range of citation counts, with an offset of 1. The citation counts from 23 Scopus subcategories were fitted to hooked power law and discretised lognormal distributions but both distributions failed a Kolmogorov-Smirnov goodness of fit test in over three quarters of cases. The discretised lognormal distribution also seems to have the wrong shape for citation distributions, with too few zeros and not enough medium values for all subjects. The cause of poor fits could be the impurity of the subject subcategories or the presence of interdisciplinary research. Although it is possible to test for subject subcategory purity indirectly through a goodness of fit test in theory with large enough sample sizes, it is probably not possible in practice. Hence it seems difficult to get conclusive evidence about the theoretically most appropriate statistical distribution.

Includes members:
  Member "publication boost in web of science journals and its effect on citation distributions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "unraveling the dynamics of growth aging and inflation for citations to scientific articles from specific research fields")
  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-2 (induced by source paper "can google scholar and mendeley help to assess the scholarly impacts of dissertations")
  Member "the discretised lognormal and hooked power law distributions for complete citation data best options for modelling and regression":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "the citation advantage of linking publications to research data")
  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 "universality of citation distributions revisited":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "testing for universality of mendeley readership distributions")
  Member "towards a new crown indicator some theoretical considerations":
    also connected through other hyperedges:
      - Hyperedge HE-8 (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)