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 "are there too many uncited articles zero inflated variants of the discretised lognormal and hooked power law distributions"):
  Abstract: Although statistical models fit many citation data sets reasonably well with the best fitting models being the hooked power law and discretised lognormal distribution, the fits are rarely close. One possible reason is that there might be more uncited articles than would be predicted by any model if some articles are inherently uncitable. Using data from 23 different Scopus categories, this article tests the assumption that removing a proportion of uncited articles from a citation dataset allows statistical distributions to have much closer fits. It also introduces two new models, zero inflated discretised lognormal distribution and the zero inflated hooked power law distribution and algorithms to fit them. In all 23 cases, the zero inflated version of the discretised lognormal distribution was an improvement on the standard version and in 16 out of 23 cases the zero inflated version of the hooked power law was an improvement on the standard version. Without zero inflation the discretised lognormal models fit the data better than the hooked power law distribution 6 out of 23 times and with it, the discretised lognormal models fit the data better than the hooked power law distribution 9 out of 23 times. Apparently uncitable articles seem to occur due to the presence of academic-related magazines in Scopus categories. In conclusion, future citation analysis and research indicators should take into account uncitable articles, and the best fitting distribution for sets of citation counts from a single subject and year is either the zero inflated discretised lognormal or zero inflated hooked power law.

Includes members:
  Member "the precision of the arithmetic mean geometric mean and percentiles for citation data an experimental simulation modelling approach":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "can google scholar and mendeley help to assess the scholarly impacts of dissertations")
  Member "are the discretised lognormal and hooked power law distributions plausible for citation data":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "mendeley reader counts for us computer science conference papers and journal articles")
  Member "the discretised lognormal and hooked power law distributions for complete citation data best options for modelling and regression":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "the citation advantage of linking publications to research data")
  Member "more precise methods for national research citation impact comparisons":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "mendeley reader counts for us computer science conference papers and journal articles")
  Member "geometric journal impact factors correcting for individual highly cited articles":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "should citations be counted separately from each originating section")
  Member "distributions for cited articles from individual subjects and years":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "how much research shared on facebook is hidden from public view a comparison of public and private online activity around plos one papers")
  Member "power laws in citation distributions evidence from scopus":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "the stability of twitter metrics a study on unavailable twitter mentions of scientific publications")
  Member "on the calculation of percentile based bibliometric indicators":
    also connected through other hyperedges:
      - Hyperedge HE-8 (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)