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 "a comparison of university performance scores and ranks by mncs and fss"):
  Abstract: In a previous article of ours, we explained the reasons why the MNCS and all similar per-publication citation indicators should not be used to measure research performance, whereas efficiency indicators (output to input) such as the FSS are valid indicators of performance. The problem frequently indicated in measuring efficiency indicators lies in the availability of input data. If we accept that such data are inaccessible, and instead resort to per-publication citation indicators, the question arises as to what extent institution performance rankings by MNCS are different from those by FSS (and so what effects such results could have on policy-makers, managers and other users of the rankings). Contrasting the 2008–2012 performance by MNCS and FSS of Italian universities in the Sciences, we try to answer that question at field, discipline, and overall university level. We present the descriptive statistics of the shifts in rank, and the correlations of both scores and ranks. The analysis reveals strong correlations in many fields but weak correlations in others. The extent of rank shifts is never negligible: a number of universities shift from top to non-top quartile ranks.

Includes members:
  Member "testing the trade off between productivity and quality in research activities":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "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")
  Member "a farewell to the mncs and like size independent indicators":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "field normalization of scientometric indicators")
  Member "efficiency of research performance and the glass researcher":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "comparison of research productivity of italian and norwegian professors and universities")
  Member "evaluating university research same performance indicator different rankings":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "field normalization of scientometric indicators")
  Member "does quantity make a difference the importance of publishing many papers":
    also connected through other hyperedges:
      (none)
  Member "ranking research institutions by the number of highly cited articles per scientist":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "comparison of research productivity of italian and norwegian professors and universities")
  Member "how do you define and measure research productivity":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "the h index for research assessment simple and popular but shown by mathematical analysis to be inconsistent and misleading")
  Member "measuring institutional research productivity for the life sciences the importance of accounting for the order of authors in the byline":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on tit for tat franceschini and maisano versus anvur regarding the italian research assessment exercise vqr 2011 2014")

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)