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 research production of nations and departments a statistical model for the share of publications"):
  Abstract: Policy makers and managers sometimes assess the share of research produced by a group (country, department, institution). This takes the form of the percentage of publications in a journal, field or broad area that has been published by the group. This quantity is affected by essentially random influences that obscure underlying changes over time and differences between groups. A model of research production is needed to help identify whether differences between two shares indicate underlying differences. This article introduces a simple production model for indicators that report the share of the world’s output in a journal or subject category, assuming that every new article has the same probability to be authored by a given group. With this assumption, confidence limits can be calculated for the underlying production capability (i.e., probability to publish). The results of a time series analysis of national contributions to 36 large monodisciplinary journals 1996–2016 are broadly consistent with this hypothesis. Follow up tests of countries and institutions in 26 Scopus subject categories support the conclusions but highlight the importance of ensuring consistent subject category coverage.

Includes members:
  Member "conceptual difficulties in the use of statistical inference in citation analysis":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the accuracy of confidence intervals for field normalised indicators")
  Member "from rankings to funnel plots the question of accounting for uncertainty when assessing university research performance":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "statistical significance and effect sizes of differences among research universities at the level of nations and worldwide based on the leiden rankings")
  Member "a multivariate stochastic model to assess research performance":
    also connected through other hyperedges:
      (none)
  Member "funnel plots for visualizing uncertainty in the research performance of institutions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a methodology to measure the effectiveness of academic recruitment and turnover")
  Member "how do you define and measure research productivity":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the h index for research assessment simple and popular but shown by mathematical analysis to be inconsistent and misleading")
  Member "the leiden ranking 2011 2012 data collection indicators and interpretation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "an evaluation of percentile measures of citation impact and a proposal for making them better")
  Member "research productivity are higher academic ranks more productive than lower ones":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "comparison of research productivity of italian and norwegian professors and universities")
  Member "research collaboration and productivity is there correlation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "an individual level assessment of the relationship between spin off activities and research performance in universities")

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)