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 counting house measuring those who count presence of bibliometrics scientometrics informetrics webometrics and altmetrics in the google scholar citations researcherid researchgate mendeley twitter"):
  Abstract: Following in the footsteps of the model of scientific communication, which has recently gone through a metamorphosis (from the Gutenberg galaxy to the Web galaxy), a change in the model and methods of scientific evaluation is also taking place. A set of new scientific tools are now providing a variety of indicators which measure all actions and interactions among scientists in the digital space, making new aspects of scientific communication emerge. In this work we present a method for ―capturing‖ the structure of an entire scientific community (the Bibliometrics, Scientometrics, Informetrics, Webometrics, and Altmetrics community) and the main agents that are part of it (scientists, documents, and sources) through the lens of Google Scholar Citations (GSC). Additionally, we compare these author ―portraits‖ to the ones offered by other profile or social platforms currently used by academics (ResearcherID, ResearchGate, Mendeley, and Twitter), in order to test their degree of use, completeness, reliability, and the validity of the information they provide. A sample of 814 authors (researchers in Bibliometrics with a public profile created in GSC) was subsequently searched in the other platforms, collecting the main indicators computed by each of them. The data collection was carried out on September, 2015. The Spearman correlation (α= 0.05) was applied to these indicators (a total of 31), and a Principal Component Analysis was carried out in order to reveal the relationships among metrics and platforms as well as the possible existence of metric clusters. We found that it is feasible to depict an accurate representation of the current state of the Bibliometrics community using data from GSC (the most influential authors, documents, journals, and publishers). Regarding the number of authors found in each platform, GSC takes the first place (814 authors), followed at a distance by ResearchGate (543), which is currently growing at a vertiginous speed. The number of Mendeley profiles is high, although 17.1% of them are basically empty. ResearcherID is also affected by this issue (34.45% of the profiles are empty), as is Twitter (47% of the Twitter accounts have published less than 100 tweets). Only 11% of our sample (93 authors) have created a profile in all the platforms analyzed in this study. From the PCA, we found two kinds of impact on the Web: first, all metrics related to academic impact. This first group can further be divided into usage metrics (views and downloads) and citation metrics. Second, all metrics related to connectivity and popularity (followers). ResearchGate indicators, as well as Mendeley readers, present a high correlation to all the indicators from GSC, but only a moderate correlation to the indicators in ResearcherID. Twitter indicators achieve only low correlations to the rest of the indicators, the highest of these being to GSC (0.42-0.46), and to Mendeley (0.41-0.46). Lastly, we present a taxonomy of all the errors that may affect the reliability of the data contained in each of these platforms, with a special emphasis in GSC, since it has been our main source of data. These errors alert us to the danger of blindly using any of these platforms for the assessment of individuals, without verifying the veracity and exhaustiveness of the data. In addition to this working paper, we also have made available a website where all the data obtained for each author and the results of the analysis of the most cited documents can be found: Scholar Mirrors.

Includes members:
  Member "methods for estimating the size of google scholar":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "coverage of highly cited documents in google scholar web of science and scopus a multidisciplinary comparison")
  Member "does google scholar contain all highly cited documents 1950 2013":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "google scholar as a data source for research assessment")
  Member "the google scholar experiment how to index false papers and manipulate bibliometric indicators":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "hunting for supernovae articles in the universe of scientometrics")
  Member "coverage and adoption of altmetrics sources in the bibliometric community":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "do altmetrics assess societal impact in a comparable way to case studies an empirical test of the convergent validity of altmetrics based on data from the uk research excellence framework ref")

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)