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: "accounting for the uncertainty in the evaluation of percentile ranks"
  Abstract: In a recent paper entitled "Inconsistencies of Recently Proposed Citation Impact Indicators and how to Avoid Them," Schreiber (2012, at arXiv:1202.3861) proposed (i) a method to assess tied ranks consistently and (ii) fractional attribution to percentile ranks in the case of relatively small samples (e.g., for n < 100). Schreiber's solution to the problem of how to handle tied ranks is convincing, in my opinion (cf. Pudovkin & Garfield, 2009). The fractional attribution, however, is computationally intensive and cannot be done manually for even moderately large batches of documents. Schreiber attributed scores fractionally to the six percentile rank classes used in the Science and Engineering Indicators of the U.S. National Science Board, and thus missed, in my opinion, the point that fractional attribution at the level of hundred percentiles-or equivalently quantiles as the continuous random variable-is only a linear, and therefore much less complex problem. Given the quantile-values, the non-linear attribution to the six classes or any other evaluation scheme is then a question of aggregation. A new routine based on these principles (including Schreiber's solution for tied ranks) is made available as software for the assessment of documents retrieved from the Web of Science (at this http URL).

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"
    - "empirical evidence for the relevance of fractional scoring in the calculation of percentile rank scores"
    - "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"
    - "citation analysis with medical subject headings mesh using the web of knowledge a new routine"

Connected through hyperedge HE-2 (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"
    - "the citation based indicator and combined impact indicator new options for measuring impact"
    - "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-3 (induced by source paper "is the new citation rank approach p100 in bibliometrics really new"):
  with members:
    - "examples for counterintuitive behavior of the new citation rank indicator p100 for bibliometric evaluations"
    - "conceptual funding of a new citation rank approach in bibliometrics p100"
    - "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"
    - "on the calculation of percentile based bibliometric indicators"

Connected through hyperedge HE-4 (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"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "on the calculation of percentile based bibliometric indicators"):
  with members:
    - "the leiden ranking 2011 2012 data collection indicators and interpretation"
    - "percentile ranks and the integrated impact indicator i3"
    - "turning the tables in citation analysis one more time principles for comparing sets of documents"

Connected through hyperedge HE-6 (induced by source paper "empirical evidence for the relevance of fractional scoring in the calculation of percentile rank scores"):
  with members:
    - "on the calculation of percentile based bibliometric indicators"
    - "percentile ranks and the integrated impact indicator i3"

Connected through hyperedge HE-7 (induced by source paper "how much do different ways of calculating percentiles influence the derived performance indicators a case study"):
  with members:
    - "on the calculation of percentile based bibliometric indicators"

Connected through hyperedge HE-8 (induced by source paper "how can journal impact factors be normalized across fields of science an assessment in terms of percentile ranks and fractional counts"):
  with members:
    - "alternatives to the journal impact factor i3 and the top 10 or top 25 of the most highly cited papers"
    - "impact factor outdated artefact or stepping stone to journal certification"
    - "percentile ranks and the integrated impact indicator i3"
    - "fractional counting of citations in research evaluation a cross and interdisciplinary assessment of the tsinghua university in beijing"
    - "universality of citation distributions revisited"
    - "an evaluation of impacts in nanoscience nanotechnology steps towards standards for citation analysis"
    - "how journal rankings can suppress interdisciplinary research a comparison between innovation studies and business management"
    - "scopus s source normalized impact per paper snip versus a journal impact factor based on fractional counting of citations"

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)