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: "normalizing the measurement of citation performance principles for comparing sets of documents"
  Abstract: Using citation analysis, sets of documents can be compared as independent samples; for example, in terms of average citation counts using potentially different reference sets. From this perspective, the size of samples matters only for the identification of significant differences and estimating margins of error. Using the percentile rank approach, differences among citation distributions can be studied non-parametrically and in a single scheme. Comparison among the sets clarifies that the different sizes of samples affect the weighing of the probabilities and therefore the rankings. We distinguish among (1) the normalization of papers against external reference sets, (2) normalization in terms of frequencies relative to the margin-totals of independent versus dependent samples, and (3) the potentially normative definition of percentile rank classes for the evaluation (e.g., top-1% most highly cited, median, etc.). When the sets to be evaluated are considered as subsamples of a single sample, the consequent citation indicator can be negatively correlated to citation indicators used hitherto.

Connected through hyperedge HE-1 (induced by source paper "a review of theory and practice in scientometrics"):
  with members:
    - "identifying research fields within business and management a journal cross citation analysis"
    - "patents as instruments for exploring innovation dynamics geographic and technological perspectives on photovoltaic cells"
    - "the operationalization of fields as wos subject categories wcs in evaluative bibliometrics the cases of library and information science and science technology studies"
    - "do altmetrics point to the broader impact of research an overview of benefits and disadvantages of altmetrics"
    - "a systematic empirical comparison of different approaches for normalizing citation impact indicators"
    - "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 revised snip indicator of elsevier s scopus"
    - "interactive overlays of journals and the measurement of interdisciplinarity on the basis of aggregated journal journal 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)