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: "publication boost in web of science journals and its effect on citation distributions"
  Abstract: In this article, we show that the dramatic increase in the number of research articles indexed in the Web of Science database impacts the commonly observed distributions of citations within these articles. First, we document that the growing number of physics articles in recent years is attributed to existing journals publishing more and more articles rather than more new journals coming into being as it happens in computer science. Second, even though the references from the more recent articles generally cover a longer time span, the newer articles are cited more frequently than the older ones if the uneven article growth is not corrected for. Nevertheless, despite this change in the distribution of citations, the citation behavior of scientists does not seem to have changed.

Connected through hyperedge HE-1 (induced by source paper "unraveling the dynamics of growth aging and inflation for citations to scientific articles from specific research fields"):
  with members:
    - "a preferential attachment paradox how does preferential attachment combine with growth to produce networks with log normal in degree distributions"
    - "growing complex network of citations of scientific papers modeling and measurements"
    - "the memory of science inflation myopia and the knowledge network"
    - "quantifying long term scientific impact"
    - "universality of citation distributions revisited"
    - "rescaling citations of publications in physics"

Connected through hyperedge HE-2 (induced by source paper "are the discretised lognormal and hooked power law distributions plausible for citation data"):
  with members:
    - "the precision of the arithmetic mean geometric mean and percentiles for citation data an experimental simulation modelling approach"
    - "the discretised lognormal and hooked power law distributions for complete citation data best options for modelling and regression"
    - "regression for citation data an evaluation of different methods"
    - "distributions for cited articles from individual subjects and years"
    - "power laws in citation distributions evidence from scopus"
    - "universality of citation distributions revisited"
    - "towards a new crown indicator some theoretical considerations"
    - "towards a new crown indicator an empirical analysis"

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)