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 "microsoft academic automatic document searches accuracy for journal articles and suitability for citation analysis"):
  Abstract: Abstract   Microsoft Academic is a free academic search engine and citation index that is similar to Google Scholar but can be automatically queried. Its data is potentially useful for bibliometric analysis if it is possible to search effectively for individual journal articles. This article compares different methods to find journal articles in its index by searching for a combination of title, authors, publication year and journal name and uses the results for the widest published correlation analysis of Microsoft Academic citation counts for journal articles so far. Based on 126,312 articles from 323 Scopus subfields in 2012, the optimal strategy to find articles with DOIs is to search for them by title and filter out those with incorrect DOIs. This finds 90% of journal articles. For articles without DOIs, the optimal strategy is to search for them by title and then filter out matches with dissimilar metadata. This finds 89% of journal articles, with an additional 1% incorrect matches. The remaining articles seem to be mainly not indexed by Microsoft Academic or indexed with a different language version of their title. From the matches, Scopus citation counts and Microsoft Academic counts have an average Spearman correlation of 0.95, with the lowest for any single field being 0.63. Thus, Microsoft Academic citation counts are almost universally equivalent to Scopus citation counts for articles that are not recent but there are national biases in the results.

Includes members:
  Member "the number of linked references of publications in microsoft academic in comparison with the web of science":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "core elements in the process of citing publications conceptual overview of the literature")
  Member "microsoft academic a multidisciplinary comparison of citation counts with scopus and mendeley for 29 journals":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "readership data and research impact")
  Member "citation analysis with microsoft academic":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "influence flowers of academic entities")
  Member "three practical field normalised alternative indicator formulae for research evaluation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the demise of single authored publications in computer science a citation network analysis")
  Member "the coverage of microsoft academic analyzing the publication output of a university":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "influence flowers of academic entities")
  Member "the journal coverage of web of science and scopus a comparative analysis":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a tale of two databases the use of web of science and scopus in academic papers")
  Member "geometric journal impact factors correcting for individual highly cited articles":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "should citations be counted separately from each originating section")
  Member "the google scholar experiment how to index false papers and manipulate bibliometric indicators":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "hunting for supernovae articles in the universe of scientometrics")

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)