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: "the accuracy of confidence intervals for field normalised indicators"
  Abstract: When comparing the average citation impact of research groups, universities and countries, field normalisation reduces the influence of discipline and time. Confidence intervals for these indicators can help with attempts to infer whether differences between sets of publications are due to chance factors. Although both bootstrapping and formulae have been proposed for these, their accuracy is unknown. In response, this article uses simulated data to systematically compare the accuracy of confidence limits in the simplest possible case, a single field and year. The results suggest that the MNLCS (Mean Normalised Log-transformed Citation Score) confidence interval formula is conservative for large groups but almost always safe, whereas bootstrap MNLCS confidence intervals tend to be accurate but can be unsafe for smaller world or group sample sizes. In contrast, bootstrap MNCS (Mean Normalised Citation Score) confidence intervals can be very unsafe, although their accuracy increases with sample sizes.

Connected through hyperedge HE-1 (induced by source paper "female citation impact superiority 1996 2018 in six out of seven english speaking nations"):
  with members:
    - "do females create higher impact research scopus citations and mendeley readers for articles from five countries"
    - "gender and research publishing in india uniformly high inequality"
    - "characterizing in text citations in scientific articles a large scale analysis"
    - "three practical field normalised alternative indicator formulae for research evaluation"
    - "the journal coverage of web of science and scopus a comparative analysis"
    - "an empirical analysis of the use of alphabetical authorship in scientific publishing"
    - "towards a new crown indicator some theoretical considerations"
    - "towards a new crown indicator an empirical analysis"

Connected through hyperedge HE-2 (induced by source paper "large publishing consortia produce higher citation impact research but co author contributions are hard to evaluate"):
  with members:
    - "granularity of algorithmically constructed publication level classifications of research publications identification of topics"
    - "three practical field normalised alternative indicator formulae for research evaluation"
    - "is collaboration among scientists related to the citation impact of papers because their quality increases with collaboration an analysis based on data from f1000prime and normalized citation scores"
    - "an empirical analysis of the use of alphabetical authorship in scientific publishing"
    - "a unified approach to mapping and clustering of bibliometric networks"

Connected through hyperedge HE-3 (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"
    - "google scholar as a data source for research assessment"
    - "can we use google scholar to identify highly cited documents"
    - "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"

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)