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: "examples for counterintuitive behavior of the new citation rank indicator p100 for bibliometric evaluations"
  Abstract: A new percentile-based rating scale P100 has recently been proposed to describe the citation impact in terms of the distribution of the unique citation values. Here I investigate P100 for 5 example datasets, two simple fictitious models and three larger empirical samples. Counterintuitive behavior is demonstrated in the model datasets, pointing to difficulties when the evolution with time of the indicator is analyzed or when different fields or publication years are compared. It is shown that similar problems can occur for the three larger datasets of empirical citation values. Further, it is observed that the performance evalution result in terms of percentiles can be influenced by selecting different journals for publication of a manuscript.

Connected through hyperedge HE-1 (induced by source paper "methods for the generation of normalized citation impact scores in bibliometrics which method best reflects the judgements of experts"):
  with members:
    - "the substantive and practical significance of citation impact differences between institutions guidelines for the analysis of percentiles using effect sizes and confidence intervals"
    - "is the new citation rank approach p100 in bibliometrics really new"
    - "on the causes of subject specific citation rates in web of science"
    - "from p100 to p100_ conception and improvement of a new citation rank approach in bibliometrics"
    - "a systematic empirical comparison of different approaches for normalizing citation impact indicators"
    - "how to calculate the practical significance of citation impact differences an empirical example from evaluative institutional bibliometrics using adjusted predictions and marginal effects"
    - "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"
    - "the wisdom of citing scientists"

Connected through hyperedge HE-2 (induced by source paper "how to improve the outcome of performance evaluations in terms of percentiles for citation frequencies of my papers"):
  with members:
    - "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"
    - "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-3 (induced by source paper "is the new citation rank approach p100 in bibliometrics really new"):
  with members:
    - "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"
    - "accounting for the uncertainty in the evaluation of percentile ranks"

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)