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: "efficiency of research performance and the glass researcher"
  Abstract: Abramo and D'Angelo (in press) doubt the validity of established size-independent indicators measuring citation impact and plead in favor of measuring scientific efficiency (by using the Fractional Scientific Strength indicator). This note is intended to comment on some questionable and a few favorable approaches in the paper by Abramo and D'Angelo (in press).

Connected through hyperedge HE-1 (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"
    - "not dead just resting the practical value of per publication citation indicators"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "research assessment by percentile based double rank analysis"):
  with members:
    - "citation score normalized by cited references csncr the introduction of a new citation impact indicator"
    - "identifying potential breakthrough publications using refined citation analyses three related explorative approaches"
    - "a review of the literature on citation impact indicators"
    - "regression for citation data an evaluation of different methods"
    - "how are excellent highly cited papers defined in bibliometrics a quantitative analysis of the literature"
    - "distributions for cited articles from individual subjects and years"
    - "brics countries and scientific excellence a bibliometric analysis of most frequently cited papers"
    - "how do you define and measure research productivity"

Connected through hyperedge HE-3 (induced by source paper "a comparison of university performance scores and ranks by mncs and fss"):
  with members:
    - "testing the trade off between productivity and quality in research activities"
    - "a farewell to the mncs and like size independent indicators"
    - "evaluating university research same performance indicator different rankings"
    - "does quantity make a difference the importance of publishing many papers"
    - "ranking research institutions by the number of highly cited articles per scientist"
    - "how do you define and measure research productivity"
    - "measuring institutional research productivity for the life sciences the importance of accounting for the order of authors in the byline"
    - "assessing the accuracy of the h and g indexes for measuring researchers 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)