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 influence of time and discipline on the magnitude of correlations between citation counts and quality scores"
  Abstract: Although various citation-based indicators are commonly used to help research evaluations, there are ongoing controversies about their value. In response, they are often correlated with quality ratings or with other quantitative indicators in order to partly assess their validity. When correlations are calculated for sets of publications from multiple disciplines or years, however, the magnitude of the correlation coefficient may be reduced, masking the strength of the underlying correlation. This article uses simulations to systematically investigate the extent to which mixing years or disciplines reduces correlations. The results show that mixing two sets of articles with different correlation strengths can reduce the correlation for the combined set to substantially below the average of the two. Moreover, even mixing two sets of articles with the same correlation strength but different mean citation counts can substantially reduce the correlation for the combined set. The extent of the reduction in correlation also depends upon whether the articles assessed have been pre-selected for being high quality and whether the relationship between the quality ratings and citation counts is linear or exponential. The results underline the importance of using homogeneous data sets but also help to interpret correlation coefficients when this is impossible.

Connected through hyperedge HE-1 (induced by source paper "can google scholar and mendeley help to assess the scholarly impacts of dissertations"):
  with members:
    - "google scholar web of science and scopus a systematic comparison of citations in 252 subject categories"
    - "on the relationships between bibliographic characteristics of scientific documents and citation and mendeley readership counts a large scale analysis of web of science publications"
    - "google scholar and the gray literature a reply to bonato s review"
    - "medical theses and derivative articles dissemination of contents and publication patterns"
    - "mendeley readership as a filtering tool to identify highly cited publications"
    - "the precision of the arithmetic mean geometric mean and percentiles for citation data an experimental simulation modelling approach"
    - "tweets vs mendeley readers how do these two social media metrics differ"
    - "do altmetrics correlate with citations extensive comparison of altmetric indicators with citations from a multidisciplinary perspective"

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)