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: "empirical evidence for the relevance of fractional scoring in the calculation of percentile rank scores"
  Abstract: Fractional scoring has been proposed to avoid inconsistencies in the attribution of publications to percentile rank classes. Uncertainties and ambiguities in the evaluation of percentile ranks can be demonstrated most easily with small data sets. But for larger data sets, an often large number of papers with the same citation count leads to the same uncertainties and ambiguities, which can be avoided by fractional scoring, demonstrated by four different empirical data sets with several thousand publications each, which are assigned to six percentile rank classes. Only by utilizing fractional scoring does, the total score of all papers exactly reproduce the theoretical value in each case.

Connected through 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"):
  with members:
    - "examples for counterintuitive behavior of the new citation rank indicator p100 for bibliometric evaluations"
    - "the normalization of citation counts based on classification systems"
    - "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"
    - "how much do different ways of calculating percentiles influence the derived performance indicators a case study"
    - "on the calculation of percentile based bibliometric indicators"
    - "the use of percentiles and percentile rank classes in the analysis of bibliometric data opportunities and limits"
    - "accounting for the uncertainty in the evaluation of percentile ranks"
    - "citation analysis with medical subject headings mesh using the web of knowledge a new routine"

Connected through hyperedge HE-2 (induced by source paper "examples for counterintuitive behavior of the new citation rank indicator p100 for bibliometric evaluations"):
  with members:
    - "conceptual funding of a new citation rank approach in bibliometrics p100"
    - "from p100 to p100_ conception and improvement of a new citation rank approach in bibliometrics"
    - "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"
    - "how much do different ways of calculating percentiles influence the derived performance indicators a case study"
    - "the use of percentiles and percentile rank classes in the analysis of bibliometric data opportunities and limits"

Connected through hyperedge HE-3 (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"):
  with members:
    - "how to calculate the practical significance of citation impact differences an empirical example from evaluative institutional bibliometrics using adjusted predictions and marginal effects"
    - "how much do different ways of calculating percentiles influence the derived performance indicators a case study"
    - "on the calculation of percentile based bibliometric indicators"
    - "the use of percentiles and percentile rank classes in the analysis of bibliometric data opportunities and limits"
    - "accounting for the uncertainty in the evaluation of percentile ranks"
    - "the citation based indicator and combined impact indicator new options for measuring impact"
    - "a further step forward in measuring journals scientific prestige the sjr2 indicator"
    - "the leiden ranking 2011 2012 data collection indicators and interpretation"

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)