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: "evaluating university research same performance indicator different rankings"
  Abstract: Assessing the research performance of multi-disciplinary institutions, where scientists belong to many fields, requires that the evaluators plan how to aggregate the performance measures of the various fields. Two methods of aggregation are possible. These are based on: (a) the performance of the individual scientists or (b) the performance of the scientific fields present in the institution. The appropriate choice depends on the evaluation context and the objectives for the particular measure. The two methods bring about differences in both the performance scores and rankings. We quantify these differences through observation of the 2008–2012 scientific production of the entire research staff employed in the hard sciences in Italian universities (over 35,000 professors). Evaluators preparing an exercise must comprehend the differences illustrated, in order to correctly select the methodologies that will achieve the evaluation objectives.

Connected through hyperedge HE-1 (induced by source paper "field normalization of scientometric indicators"):
  with members:
    - "relative citation ratio rcr an empirical attempt to study a new field normalized bibliometric indicator"
    - "citation analysis with microsoft academic"
    - "three practical field normalised alternative indicator formulae for research evaluation"
    - "a comparison of university performance scores and ranks by mncs and fss"
    - "a farewell to the mncs and like size independent indicators"
    - "citation score normalized by cited references csncr the introduction of a new citation impact indicator"
    - "national disciplinary and temporal variations in the extent to which articles with more authors have more impact evidence from a geometric field normalised citation indicator"
    - "quality versus quantity in scientific impact"

Connected through hyperedge HE-2 (induced by source paper "factors influencing cities publishing efficiency"):
  with members:
    - "the relationship among research productivity research collaboration and their determinants"
    - "the sum of it all revealing collaboration patterns by combining authorship and acknowledgements"
    - "a spatial scientometric analysis of the publication output of cities worldwide"
    - "the journal coverage of web of science and scopus a comparative analysis"
    - "the european union china and the united states in the top 1 and top 10 layers of most frequently cited publications competition and collaborations"
    - "international scientific collaboration of china collaborating countries institutions and individuals"
    - "mapping excellence in the geography of science an approach based on scopus data"
    - "the detection of hot regions in the geography of science a visualization approach by using density maps"

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"
    - "efficiency of research performance and the glass researcher"
    - "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)