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: "is the new citation rank approach p100 in bibliometrics really new"
  Abstract: The percentile-based rating scale P100 describes the citation impact in terms of the distribution of unique citation values. This approach has recently been refined by considering also the frequency of papers with the same citation counts. Here I compare the resulting P100′ with P100 for an empirical dataset and a simple fictitious model dataset. It is shown that P100′ is not much different from standard percentile-based ratings in terms of citation frequencies. A new indicator P100″ is introduced.

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"
    - "examples for counterintuitive behavior of the new citation rank indicator p100 for bibliometric evaluations"
    - "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"

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)