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: "the citation based indicator and combined impact indicator new options for measuring impact"
  Abstract: Metrics based on percentile ranks (PRs) for measuring scholarly impact involves complex treatment because of various defects such as overvaluing or devaluing an object caused by percentile ranking schemes, ignoring precise citation variation among those ranked next to each other, and inconsistency caused by additional papers or citations. These defects are especially obvious in a small-sized dataset. To avoid the complicated treatment of PRs based metrics, we propose two new indicators - the citation-based indicator (CBI) and the combined impact indicator (CII). Document types of publications are taken into account. With the two indicators, one would no more be bothered by complex issues encountered by PRs based indicators. For a small-sized dataset with less than 100 papers, special calculation is no more needed. The CBI is based solely on citation counts and the CII measures the integrate contributions of publications and citations. Both virtual and empirical data are used so as to compare the effect of related indicators. The CII and the PRs based indicator I3 are highly correlated but the former reflects citation impact more and the latter relates more to publications.

Connected through hyperedge HE-1 (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"):
  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"
    - "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 leiden ranking 2011 2012 data collection indicators and interpretation"
    - "percentile ranks and the integrated impact indicator i3"
    - "caveats for the journal and field normalizations in the cwts leiden evaluations of research performance"
    - "rivals for the crown reply to opthof and leydesdorff"

Connected through hyperedge HE-2 (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:
    - "empirical evidence for the relevance of fractional scoring in the calculation of percentile rank scores"
    - "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"
    - "a further step forward in measuring journals scientific prestige the sjr2 indicator"
    - "the leiden ranking 2011 2012 data collection indicators and interpretation"

Connected through hyperedge HE-3 (induced by source paper "accounting for the uncertainty in the evaluation of percentile ranks"):
  with members:
    - "percentile ranks and the integrated impact indicator i3"

Connected through hyperedge HE-4 (induced by source paper "percentile ranks and the integrated impact indicator i3"):
  with members:
    - "which cities produce excellent papers worldwide more than can be expected a new mapping approach using google maps based on statistical significance testing"

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)