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: "citation count distributions for large monodisciplinary journals"
  Abstract: Many different citation-based indicators are used by researchers and research evaluators to help evaluate the impact of scholarly outputs. Although the appropriateness of individual citation indicators depends in part on the statistical properties of citation counts, there is no universally agreed best-fitting statistical distribution against which to check them. The two current leading candidates are the discretised lognormal and the hooked or shifted power law. These have been mainly tested on sets of articles from a single field and year but these collections can include multiple specialisms that might dilute their properties. This article fits statistical distributions to 50 large subject-specific journals in the belief that individual journals can be purer than subject categories and may therefore give clearer findings. The results show that in most cases the discretised lognormal fits significantly better than the hooked power law, reversing previous findings for entire subcategories. This suggests that the discretised lognormal is the more appropriate distribution for modelling pure citation data. Thus, future analytical investigations of the properties of citation indicators can use the lognormal distribution to analyse their basic properties. This article also includes improved software for fitting the hooked power law.

Connected through hyperedge HE-1 (induced by source paper "evaluating research and researchers by the journal impact factor is it better than coin flipping"):
  with members:
    - "systematic analysis of agreement between metrics and peer review in the uk ref"
    - "probability and expected frequency of breakthroughs a robust method of research assessment based on the double rank property of citation distributions"
    - "core elements in the process of citing publications conceptual overview of the literature"
    - "use of the journal impact factor for assessing individual articles need not be wrong"
    - "can the journal impact factor be used as a criterion for the selection of junior researchers a large scale empirical study based on researcherid data"
    - "citation success index an intuitive pair wise journal comparison metric"
    - "citations indicators of quality the impact fallacy"
    - "a review of the literature on citation impact indicators"

Connected through hyperedge HE-2 (induced by source paper "on the heterogeneous distributions in paper citations"):
  with members:
    - "skewness of citation impact data and covariates of citation distributions a large scale empirical analysis based on web of science data"
    - "citations indicators of quality the impact fallacy"
    - "large scale analysis of the accuracy of the journal classification systems of web of science and scopus"
    - "predicting the long term citation impact of recent publications"
    - "a review of the literature on citation impact indicators"
    - "distributions for cited articles from individual subjects and years"
    - "power laws in citation distributions evidence from scopus"

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"
    - "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"
    - "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 the discretised lognormal and hooked power law distributions plausible for citation data"
    - "are there too many uncited articles zero inflated variants of the discretised lognormal and hooked power law distributions"
    - "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"

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)