human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "whole counting vs whole normalized counting a country level comparative study of internationally collaborated papers on tribology"):
  Abstract: The purpose of this study is to compare the changing behavior of two counting methods (whole counting and whole-normalized counting) and inflation rate at country level research productivity and impact. For this, publication data on tribology research published between 1998 and 2012 from SCOPUS has been used. Only internationally collaborated papers are considered for comparison between two counting methods. The result of correlation tests shows that there is highly correlation in all the four indicators between the two counting methods. However, the result of t-test shows that there is significant difference in the three indicators (paper count, citation count and h-index) between the two counting methods. This study concludes that whole-normalized counting (fractional) is the better choice for publication and citations counting at the country level assessment.

Includes members:
  Member "a macro level scientometric analysis of world tribology research output 1998 2012":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "study of citation networks in tribology research")
  Member "the leiden ranking 2011 2012 data collection indicators and interpretation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "an evaluation of percentile measures of citation impact and a proposal for making them better")

Question: Please tell me which class the source paper 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)