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: "google scholar as a data source for research assessment"
  Abstract: The launch of Google Scholar (GS) marked the beginning of a revolution in the scientific information market. This search engine, unlike traditional databases, automatically indexes information from the academic web. Its ease of use, together with its wide coverage and fast indexing speed, have made it the first tool most scientists currently turn to when they need to carry out a literature search. Additionally, the fact that its search results were accompanied from the beginning by citation counts, as well as the later development of secondary products which leverage this citation data (such as Google Scholar Metrics and Google Scholar Citations), made many scientists wonder about its potential as a source of data for bibliometric analyses. The goal of this chapter is to lay the foundations for the use of GS as a supplementary source (and in some disciplines, arguably the best alternative) for scientific evaluation. First, we present a general overview of how GS works. Second, we present empirical evidences about its main characteristics (size, coverage, and growth rate). Third, we carry out a systematic analysis of the main limitations this search engine presents as a tool for the evaluation of scientific performance. Lastly, we discuss the main differences between GS and other more traditional bibliographic databases in light of the correlations found between their citation data. We conclude that Google Scholar presents a broader view of the academic world because it has brought to light a great amount of sources that were not previously visible.

Connected through hyperedge HE-1 (induced by source paper "coverage of highly cited documents in google scholar web of science and scopus a multidisciplinary comparison"):
  with members:
    - "evidence of open access of scientific publications in google scholar a large scale analysis"
    - "can we use google scholar to identify highly cited documents"
    - "the accuracy of confidence intervals for field normalised indicators"
    - "three practical field normalised alternative indicator formulae for research evaluation"
    - "a two sided academic landscape portrait of highly cited documents in google scholar 1950 2013"
    - "the journal coverage of web of science and scopus a comparative analysis"
    - "methods for estimating the size of google scholar"
    - "a new methodology for comparing google scholar and scopus"

Connected through hyperedge HE-2 (induced by source paper "google scholar web of science and scopus a systematic comparison of citations in 252 subject categories"):
  with members:
    - "coverage of highly cited documents in google scholar web of science and scopus a multidisciplinary comparison"
    - "the journal coverage of web of science and scopus a comparative analysis"
    - "methods for estimating the size of google scholar"
    - "a new methodology for comparing google scholar and scopus"
    - "the google scholar experiment how to index false papers and manipulate bibliometric indicators"

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)