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 "the google scholar experiment how to index false papers and manipulate bibliometric indicators"):
  Abstract: Google Scholar has been well received by the research community. Its promises of free, universal and easy access to scientific literature as well as the perception that it covers better than other traditional multidisciplinary databases the areas of the Social Sciences and the Humanities have contributed to the quick expansion of Google Scholar Citations and Google Scholar Metrics: two new bibliometric products that offer citation data at the individual level and at journal level. In this paper we show the results of a experiment undertaken to analyze Google Scholar's capacity to detect citation counting manipulation. For this, six documents were uploaded to an institutional web domain authored by a false researcher and referencing all the publications of the members of the EC3 research group at the University of Granada. The detection of Google Scholar of these papers outburst the citations included in the Google Scholar Citations profiles of the authors. We discuss the effects of such outburst and how it could affect the future development of such products not only at individual level but also at journal level, especially if Google Scholar persists with its lack of transparency.

Includes members:
  Member "ranking journals could google scholar metrics be an alternative to journal citation reports and scimago journal rank":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "google scholar as a data source for research assessment")
  Member "manipulating google scholar citations and google scholar metrics simple easy and tempting":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "implementing recommendation algorithms in a large scale biomedical science knowledge base")
  Member "the inconsistency of the h index":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "the h index for research assessment simple and popular but shown by mathematical analysis to be inconsistent and misleading")

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)