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 "do all citations value the same valuing citations by the value of the citing items"):
  Abstract: Bibliometricians have long recurred to citation counts to measure the impact of publications on the advancement of science. However, since the earliest days of the field, some scholars have questioned whether all citations should value the same, and have gone on to weight them by a variety of factors. However sophisticated the operationalization of the measures, the methodologies used in weighting citations still present limits in their underlying assumptions. This work takes an alternate approach to resolving the underlying problem: the proposal is to value citations by the impact of the citing articles. As well as conceptualizing a new indicator of impact, the work illustrates its application to the 2004-2012 Italian scientific production indexed in the WoS. The new indicator appears highly correlated to traditional field normalized citations, however the shifts observed between the two measures are frequent and the number of outliers not at all negligible. Moreover, the new indicator seems to show greater "sensitivity" when used in identification of the top-cited papers.

Includes members:
  Member "revisiting the scientometric conceptualization of impact and its measurement":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "peer review versus bibliometrics which method better predicts the scholarly impact of publications")
  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 "refrain from adopting the combination of citation and journal metrics to grade publications as used in the italian national research assessment exercise vqr 2011 2014":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "peer review versus bibliometrics which method better predicts the scholarly impact of publications")
  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 "a further step forward in measuring journals scientific prestige the sjr2 indicator":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "prevalence of potentially predatory publishing in scopus on the country level")
  Member "revisiting the scaling of citations for research assessment":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a nation s foreign and domestic professors which have better research performance the italian case")
  Member "applying weighted pagerank to author citation networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "assigning credit to scientific datasets using article citation networks")
  Member "assessing the varying level of impact measurement accuracy as a function of the citation window length":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "should citations be field normalized in evaluative bibliometrics an empirical analysis based on propensity score matching")

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)