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: "skewness of citation impact data and covariates of citation distributions a large scale empirical analysis based on web of science data"
  Abstract: Using percentile shares, one can visualize and analyze the skewness in bibliometric data across disciplines and over time. The resulting figures can be intuitively interpreted and are more suitable for detailed analysis of the effects of independent and control variables on distributions than regression analysis. We show this by using percentile shares to analyze so-called "factors influencing citation impact" (FICs; e.g., the impact factor of the publishing journal) across year and disciplines. All articles (n= 2,961,789) covered by WoS in 1990 (n= 637,301), 2000 (n= 919,485), and 2010 (n= 1,405,003) are used. In 2010, nearly half of the citation impact is accounted for by the 10% most-frequently cited papers; the skewness is largest in the humanities (68.5% in the top-10% layer) and lowest in agricultural sciences (40.6%). The comparison of the effects of the different FICs (the number of cited references, number of authors, number of pages, and JIF) on citation impact shows that JIF has indeed the strongest correlations with the citation scores. However, the correlation between FICs and citation impact is lower, if citations are normalized instead of using raw citation counts.

Connected through hyperedge HE-1 (induced by source paper "testing for universality of mendeley readership distributions"):
  with members:
    - "predicting publication long term impact through a combination of early citations and journal impact factor"
    - "revisiting the scientometric conceptualization of impact and its measurement"
    - "an investigation on the skewness patterns and fractal nature of research productivity distributions at field and discipline level"
    - "scholarly use of social media and altmetrics a review of the literature"
    - "do altmetrics correlate with citations extensive comparison of altmetric indicators with citations from a multidisciplinary perspective"
    - "how well developed are altmetrics a cross disciplinary analysis of the presence of alternative metrics in scientific publications"
    - "revisiting the scaling of citations for research assessment"
    - "altmetrics in the wild using social media to explore scholarly impact"

Connected through hyperedge HE-2 (induced by source paper "on the heterogeneous distributions in paper citations"):
  with members:
    - "citation count distributions for large monodisciplinary journals"
    - "citations indicators of quality the impact fallacy"
    - "large scale analysis of the accuracy of the journal classification systems of web of science and scopus"
    - "predicting the long term citation impact of recent publications"
    - "a review of the literature on citation impact indicators"
    - "distributions for cited articles from individual subjects and years"
    - "power laws in citation distributions evidence from scopus"

Connected through hyperedge HE-3 (induced by source paper "an investigation on the skewness patterns and fractal nature of research productivity distributions at field and discipline level"):
  with members:
    - "a farewell to the mncs and like size independent indicators"
    - "how do you define and measure research productivity"
    - "the importance of accounting for the number of co authors and their order when assessing research performance at the individual level in the life sciences"
    - "revisiting the scaling of citations for research assessment"
    - "a heuristic approach to author name disambiguation in bibliometrics databases for large scale research assessments"
    - "the skewness of computer science"

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)