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: "not dead just resting the practical value of per publication citation indicators"
  Abstract: In the final analysis citation-based indicators are inferior to effective peer review and even peer review is flawed. It is impossible to accurately measure the value or impact of scientific research and a key task of scientometricians should be to produce figures for policy makers and others that are as informative as it is practical to make them and to ensure that users are fully aware of their limitations. Although the Abramo and D'Angelo (2016) suggestions make a lot of theoretical sense and so are a goal that is worth aiming for, it is unrealistic in practice to advocate their universal use in the contexts discussed above. This is because the indicators would still have flaws in addition to the generic limitations of citation-based indicators and would still be inadequate for replacing peer review. Thus, the expense of the data gathering does not always justify the value in practice of the extra accuracy. In the longer term, the restructuring of education needed in order to get the homogeneity necessary for genuinely comparable statistics would be too expensive and probably damaging to the research mission, in addition to being out of proportion to the likely value of any citation-based indicator.

Connected through hyperedge HE-1 (induced by source paper "the h index for research assessment simple and popular but shown by mathematical analysis to be inconsistent and misleading"):
  with members:
    - "like for like bibliometric substitutes for peer review advantages and limits of indicators calculated from the ep index"
    - "research assessment by percentile based double rank analysis"
    - "how do you define and measure research productivity"
    - "the inconsistency of the h index"

Connected through hyperedge HE-2 (induced by source paper "comparison of research productivity of italian and norwegian professors and universities"):
  with members:
    - "predicting publication long term impact through a combination of early citations and journal impact factor"
    - "the impact of unproductive and top researchers on overall university research performance"
    - "the elephant in the room the problem of quantifying productivity in evaluative scientometrics"
    - "the ratio of top scientists to the academic staff as an indicator of the competitive strength of universities"
    - "efficiency of research performance and the glass researcher"
    - "ranking research institutions by the number of highly cited articles per scientist"
    - "predicting the long term citation impact of recent publications"
    - "how do you define and measure research productivity"

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)