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: "identifying research fields within business and management a journal cross citation analysis"
  Abstract: A discipline such as business and management (BM second, for the purpose of normalising citation data as it is well-known that citation rates vary significantly between different disciplines. And third, because journal rankings and lists tend to split their classifications into different subjects—for example, the Association of Business Schools list, which is a standard in the UK, has 22 different fields. Unfortunately, at the moment these are created in an ad-hoc manner with no underlying rigour. The purpose of this paper is to identify possible sub-fields in B&M rigorously based on actual citation patterns. We have examined 450 journals in B&M, which are included in the ISI Web of Science and analysed the cross-citation rates between them enabling us to generate sets of coherent and consistent sub-fields that minimise the extent to which journals appear in several categories. Implications and limitations of the analysis are discussed.

Connected through hyperedge HE-1 (induced by source paper "evaluating journal quality a review of journal citation indicators and ranking in business and management"):
  with members:
    - "a review of theory and practice in scientometrics"
    - "comprehensive indicator comparisons intelligible to non experts the case of two snip versions"
    - "a review of the literature on citation impact indicators"
    - "aggregated journal journal citation relations in scopus and web of science matched and compared in terms of networks maps and interactive overlays"
    - "methods for the generation of normalized citation impact scores in bibliometrics which method best reflects the judgements of experts"
    - "the operationalization of fields as wos subject categories wcs in evaluative bibliometrics the cases of library and information science and science technology studies"
    - "a systematic empirical comparison of different approaches for normalizing citation impact indicators"
    - "the use of percentiles and percentile rank classes in the analysis of bibliometric data opportunities and limits"

Connected through hyperedge HE-2 (induced by source paper "a review of theory and practice in scientometrics"):
  with members:
    - "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"
    - "how to improve the prediction based on citation impact percentiles for years shortly after the publication date"

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)