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: "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.

Connected through hyperedge HE-1 (induced by source paper "mendeley reader counts for us computer science conference papers and journal articles"):
  with members:
    - "google scholar web of science and scopus a systematic comparison of citations in 252 subject categories"
    - "on the relationships between bibliographic characteristics of scientific documents and citation and mendeley readership counts a large scale analysis of web of science publications"
    - "could scientists use altmetric com scores to predict longer term citation counts"
    - "which type of citation analysis generates the most accurate taxonomy of scientific and technical knowledge"
    - "mendeley readership as a filtering tool to identify highly cited publications"
    - "role of interdisciplinarity in computer sciences quantification impact and life trajectory"
    - "more precise methods for national research citation impact comparisons"
    - "tweets vs mendeley readers how do these two social media metrics differ"

Connected through hyperedge HE-2 (induced by source paper "the citation advantage of foreign language references for chinese social science papers"):
  with members:
    - "regression for citation data an evaluation of different methods"
    - "comparing journals from different fields of science and social science through a jcr subject categories normalized impact factor"
    - "how to improve the prediction based on citation impact percentiles for years shortly after the publication date"
    - "self selected or mandated open access increases citation impact for higher quality research"

Connected through hyperedge HE-3 (induced by source paper "three practical field normalised alternative indicator formulae for research evaluation"):
  with members:
    - "the precision of the arithmetic mean geometric mean and percentiles for citation data an experimental simulation modelling approach"
    - "the discretised lognormal and hooked power law distributions for complete citation data best options for modelling and regression"
    - "are there too many uncited articles zero inflated variants of the discretised lognormal and hooked power law distributions"
    - "national disciplinary and temporal variations in the extent to which articles with more authors have more impact evidence from a geometric field normalised citation indicator"
    - "citation count distributions for large monodisciplinary journals"
    - "more precise methods for national research citation impact comparisons"
    - "national research impact indicators from mendeley readers"
    - "on the calculation of percentile based bibliometric indicators"

Connected through hyperedge HE-4 (induced by source paper "the accuracy of confidence intervals for field normalised indicators"):
  with members:
    - "use of the journal impact factor for assessing individual articles need not be wrong"
    - "three practical field normalised alternative indicator formulae for research evaluation"
    - "conceptual difficulties in the use of statistical inference in citation analysis"
    - "are there too many uncited articles zero inflated variants of the discretised lognormal and hooked power law distributions"
    - "citation count distributions for large monodisciplinary journals"
    - "national research impact indicators from mendeley readers"
    - "predicting the long term citation impact of recent publications"
    - "regression for citation data an evaluation of different methods"

Connected through hyperedge HE-5 (induced by source paper "use of the journal impact factor for assessing individual articles need not be wrong"):
  with members:
    - "the discretised lognormal and hooked power law distributions for complete citation data best options for modelling and regression"
    - "a review of the literature on citation impact indicators"
    - "a further step forward in measuring journals scientific prestige the sjr2 indicator"
    - "impact factor outdated artefact or stepping stone to journal certification"
    - "some modifications to the snip journal impact indicator"
    - "universality of citation distributions revisited"
    - "citations versus journal impact factor as proxy of quality could the latter ever be preferable"
    - "measuring contextual citation impact of scientific journals"

Connected through hyperedge HE-6 (induced by source paper "are there too many uncited articles zero inflated variants of the discretised lognormal and hooked power law distributions"):
  with members:
    - "the precision of the arithmetic mean geometric mean and percentiles for citation data an experimental simulation modelling approach"
    - "the discretised lognormal and hooked power law distributions for complete citation data best options for modelling and regression"
    - "more precise methods for national research citation impact comparisons"
    - "geometric journal impact factors correcting for individual highly cited articles"
    - "distributions for cited articles from individual subjects and years"
    - "power laws in citation distributions evidence from scopus"
    - "on the calculation of percentile based bibliometric indicators"
    - "the leiden ranking 2011 2012 data collection indicators and interpretation"

Connected through hyperedge HE-7 (induced by source paper "citation count distributions for large monodisciplinary journals"):
  with members:
    - "the discretised lognormal and hooked power law distributions for complete citation data best options for modelling and regression"
    - "geometric journal impact factors correcting for individual highly cited articles"
    - "regression for citation data an evaluation of different methods"
    - "distributions for cited articles from individual subjects and years"
    - "power laws in citation distributions evidence from scopus"
    - "quantifying long term scientific impact"
    - "towards a new crown indicator some theoretical considerations"

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.DL(Digital Libraries)