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 "which percentile based approach should be preferred for calculating normalized citation impact values an empirical comparison of five approaches including a newly developed citation rank approach p100"):
  Abstract: Percentile-based approaches have been proposed as a non-parametric alternative to parametric central-tendency statistics to normalize observed citation counts. Percentiles are based on an ordered set of citation counts in a reference set, whereby the fraction of papers at or below the citation counts of a focal paper is used as an indicator for its relative citation impact in the set. In this study, we pursue two related objectives: (1) although different percentile-based approaches have been developed, an approach is hitherto missing that satisfies a number of criteria such as scaling of the percentile ranks from zero (all other papers perform better) to 100 (all other papers perform worse), and solving the problem with tied citation ranks unambiguously. We introduce a new citation-rank approach having these properties, namely P100. (2) We compare the reliability of P100 empirically with other percentile-based approaches, such as the approaches developed by the SCImago group, the Centre for Science and Technology Studies (CWTS), and Thomson Reuters (InCites), using all papers published in 1980 in Thomson Reuters Web of Science (WoS). How accurately can the different approaches predict the long-term citation impact in 2010 (in year 31) using citation impact measured in previous time windows (years 1 to 30)? The comparison of the approaches shows that the method used by InCites overestimates citation impact (because of using the highest percentile rank when papers are assigned to more than a single subject category) whereas the SCImago indicator shows higher power in predicting the long-term citation impact on the basis of citation rates in early years. Since the results show a disadvantage in this predictive ability for P100 against the other approaches, there is still room for further improvements.

Includes members:
  Member "empirical evidence for the relevance of fractional scoring in the calculation of percentile rank scores":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "how to improve the outcome of performance evaluations in terms of percentiles for citation frequencies of my papers")
  Member "how to calculate the practical significance of citation impact differences an empirical example from evaluative institutional bibliometrics using adjusted predictions and marginal effects":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "do altmetrics correlate with the quality of papers a large scale empirical study based on f1000prime data")
  Member "how much do different ways of calculating percentiles influence the derived performance indicators a case study":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "field normalization of scientometric indicators")
  Member "on the calculation of percentile based bibliometric indicators":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "an evaluation of percentile measures of citation impact and a proposal for making them better")
  Member "the use of percentiles and percentile rank classes in the analysis of bibliometric data opportunities and limits":
    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 "accounting for the uncertainty in the evaluation of percentile ranks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "how to improve the outcome of performance evaluations in terms of percentiles for citation frequencies of my papers")
  Member "the citation based indicator and combined impact indicator new options for measuring impact":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "the substantive and practical significance of citation impact differences between institutions guidelines for the analysis of percentiles using effect sizes and confidence intervals")
  Member "a further step forward in measuring journals scientific prestige the sjr2 indicator":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "prevalence of potentially predatory publishing in scopus on the country level")

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)