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: "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.

Connected through hyperedge HE-1 (induced by source paper "three practical field normalised alternative indicator formulae for research evaluation"):
  with members:
    - "the precision of the arithmetic mean geometric mean and percentiles for citation data an experimental simulation modelling approach"
    - "are the discretised lognormal and hooked power law distributions plausible for citation data"
    - "the discretised lognormal and hooked power law distributions for complete citation data best options for modelling and regression"
    - "national disciplinary and temporal variations in the extent to which articles with more authors have more impact evidence from a geometric field normalised citation indicator"
    - "citation count distributions for large monodisciplinary journals"
    - "more precise methods for national research citation impact comparisons"
    - "national research impact indicators from mendeley readers"
    - "on the calculation of percentile based bibliometric indicators"

Connected through hyperedge HE-2 (induced by source paper "the accuracy of confidence intervals for field normalised indicators"):
  with members:
    - "use of the journal impact factor for assessing individual articles need not be wrong"
    - "three practical field normalised alternative indicator formulae for research evaluation"
    - "conceptual difficulties in the use of statistical inference in citation analysis"
    - "are the discretised lognormal and hooked power law distributions plausible for citation data"
    - "citation count distributions for large monodisciplinary journals"
    - "national research impact indicators from mendeley readers"
    - "predicting the long term citation impact of recent publications"
    - "regression for citation data an evaluation of different methods"

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)