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: "more precise methods for national research citation impact comparisons"
  Abstract: Governments sometimes need to analyse sets of research papers within a field in order to monitor progress, assess the effect of recent policy changes, or identify areas of excellence. They may compare the average citation impacts of the papers by dividing them by the world average for the field and year. Since citation data is highly skewed, however, simple averages may be too imprecise to robustly identify differences within, rather than across, fields. In response, this article introduces two new methods to identify national differences in average citation impact, one based on linear modelling for normalised data and the other using the geometric mean. Results from a sample of 26 Scopus fields between 2009-2015 show that geometric means are the most precise and so are recommended for smaller sample sizes, such as for individual fields. The regression method has the advantage of distinguishing between national contributions to internationally collaborative articles, but has substantially wider confidence intervals than the geometric mean, undermining its value for any except the largest sample sizes.

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"
    - "are the discretised lognormal and hooked power law distributions plausible for citation data"
    - "tweets vs mendeley readers how do these two social media metrics differ"

Connected through hyperedge HE-2 (induced by source paper "field normalization of scientometric indicators"):
  with members:
    - "relative citation ratio rcr an empirical attempt to study a new field normalized bibliometric indicator"
    - "citation analysis with microsoft academic"
    - "three practical field normalised alternative indicator formulae for research evaluation"
    - "a comparison of university performance scores and ranks by mncs and fss"
    - "a farewell to the mncs and like size independent indicators"
    - "citation score normalized by cited references csncr the introduction of a new citation impact indicator"
    - "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"
    - "quality versus quantity in scientific impact"

Connected through hyperedge HE-3 (induced by source paper "dimensions a competitor to scopus and the web of science"):
  with members:
    - "microsoft academic automatic document searches accuracy for journal articles and suitability for citation analysis"
    - "can we use google scholar to identify highly cited documents"
    - "citation analysis with microsoft academic"
    - "the journal coverage of web of science and scopus a comparative analysis"
    - "a review of the literature on citation impact indicators"
    - "tweets vs mendeley readers how do these two social media metrics differ"
    - "do altmetrics correlate with citations extensive comparison of altmetric indicators with citations from a multidisciplinary perspective"
    - "the google scholar experiment how to index false papers and manipulate bibliometric indicators"

Connected through hyperedge HE-4 (induced by source paper "can microsoft academic assess the early citation impact of in press articles a multi discipline exploratory analysis"):
  with members:
    - "microsoft academic automatic document searches accuracy for journal articles and suitability for citation analysis"
    - "microsoft academic a multidisciplinary comparison of citation counts with scopus and mendeley for 29 journals"
    - "citation analysis with microsoft academic"
    - "three practical field normalised alternative indicator formulae for research evaluation"
    - "influencia de la fecha de publicacion online de los articulos cientificos en los indicadores bibliometricos"
    - "the coverage of microsoft academic analyzing the publication output of a university"
    - "when is an article actually published an analysis of online availability publication and indexation dates"
    - "plagiarism detection in arxiv"

Connected through hyperedge HE-5 (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"
    - "are the discretised lognormal and hooked power law distributions plausible for citation data"
    - "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"
    - "national research impact indicators from mendeley readers"
    - "on the calculation of percentile based bibliometric indicators"

Connected through hyperedge HE-6 (induced by source paper "the precision of the arithmetic mean geometric mean and percentiles for citation data an experimental simulation modelling approach"):
  with members:
    - "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"
    - "the vqr italy s second national research assessment methodological failures and ranking distortions"
    - "the relationship between the number of authors of a publication its citations and the impact factor of the publishing journal evidence from italy"
    - "national research impact indicators from mendeley readers"
    - "tweets vs mendeley readers how do these two social media metrics differ"
    - "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"

Connected through hyperedge HE-7 (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"
    - "are the discretised lognormal and hooked power law distributions plausible for citation data"
    - "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"
    - "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-8 (induced by source paper "a farewell to the mncs and like size independent indicators"):
  with members:
    - "ranking research institutions by the number of highly cited articles per scientist"
    - "how do you define and measure research productivity"
    - "assessing the accuracy of the h and g indexes for measuring researchers productivity"
    - "national scale research performance assessment at the individual level"
    - "cwts crown indicator measures citation impact of a research group s publication oeuvre"
    - "towards a new crown indicator some theoretical considerations"
    - "caveats for the journal and field normalizations in the cwts leiden evaluations of research performance"
    - "remaining problems with the new crown indicator mncs of the cwts"

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)