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: "integrated impact indicators i3 compared with impact factors ifs an alternative research design with policy implications"
  Abstract: In bibliometrics, the association of "impact" with central-tendency statistics is mistaken. Impacts add up, and citation curves should therefore be integrated instead of averaged. For example, the journals MIS Quarterly and JASIST differ by a factor of two in terms of their respective impact factors (IF), but the journal with the lower IF has the higher impact. Using percentile ranks (e.g., top-1%, top-10%, etc.), an integrated impact indicator (I3) can be based on integration of the citation curves, but after normalization of the citation curves to the same scale. The results across document sets can be compared as percentages of the total impact of a reference set. Total number of citations, however, should not be used instead because the shape of the citation curves is then not appreciated. I3 can be applied to any document set and any citation window. The results of the integration (summation) are fully decomposable in terms of journals or instititutional units such as nations, universities, etc., because percentile ranks are determined at the paper level. In this study, we first compare I3 with IFs for the journals in two ISI Subject Categories ("Information Science & Library Science" and "Multidisciplinary Sciences"). The LIS set is additionally decomposed in terms of nations. Policy implications of this possible paradigm shift in citation impact analysis are specified.

Connected through hyperedge HE-1 (induced by source paper "a review of the literature on citation impact indicators"):
  with members:
    - "a sensitivity analysis of researchers productivity rankings to the time of citation observation"
    - "the precision of the arithmetic mean geometric mean and percentiles for citation data an experimental simulation modelling approach"
    - "the journal coverage of web of science and scopus a comparative analysis"
    - "measuring academic influence not all citations are equal"
    - "a review of theory and practice in scientometrics"
    - "comprehensive indicator comparisons intelligible to non experts the case of two snip versions"
    - "methods for estimating the size of google scholar"
    - "large scale analysis of the accuracy of the journal classification systems of web of science and scopus"

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)