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: "normalization of zero inflated data an empirical analysis of a new indicator family and its use with altmetrics data"
  Abstract: Recently, two new indicators (Equalized Mean-based Normalized Proportion Cited, EMNPC; Mean-based Normalized Proportion Cited, MNPC) were proposed which are intended for sparse scientometrics data. The indicators compare the proportion of mentioned papers (e.g. on Facebook) of a unit (e.g., a researcher or institution) with the proportion of mentioned papers in the corresponding fields and publication years (the expected values). In this study, we propose a third indicator (Mantel-Haenszel quotient, MHq) belonging to the same indicator family. The MHq is based on the MH analysis - an established method in statistics for the comparison of proportions. We test (using citations and assessments by peers, i.e. F1000Prime recommendations) if the three indicators can distinguish between different quality levels as defined on the basis of the assessments by peers. Thus, we test their convergent validity. We find that the indicator MHq is able to distinguish between the quality levels in most cases while MNPC and EMNPC are not. Since the MHq is shown in this study to be a valid indicator, we apply it to six types of zero-inflated altmetrics data and test whether different altmetrics sources are related to quality. The results for the various altmetrics demonstrate that the relationship between altmetrics (Wikipedia, Facebook, blogs, and news data) and assessments by peers is not as strong as the relationship between citations and assessments by peers. Actually, the relationship between citations and peer assessments is about two to three times stronger than the association between altmetrics and assessments by peers.

Connected through hyperedge HE-1 (induced by source paper "do altmetrics assess societal impact in a comparable way to case studies an empirical test of the convergent validity of altmetrics based on data from the uk research excellence framework ref"):
  with members:
    - "field and time normalization of data with many zeros an empirical analysis using citation and twitter data"
    - "measuring field normalized impact of papers on specific societal groups an altmetrics study based on mendeley data"
    - "how many scientific papers are mentioned in policy related documents an empirical investigation using web of science and altmetric data"
    - "grand challenges in altmetrics heterogeneity data quality and dependencies"
    - "policy documents as sources for measuring societal impact how often is climate change research mentioned in policy related documents"
    - "scholarly use of social media and altmetrics a review of the literature"
    - "influence of study type on twitter activity for medical research papers"
    - "validity of altmetrics data for measuring societal impact a study using data from altmetric and f1000prime"

Connected through hyperedge HE-2 (induced by source paper "does the public discuss other topics on climate change than researchers a comparison of explorative networks based on author keywords and hashtags"):
  with members:
    - "do altmetrics correlate with the quality of papers a large scale empirical study based on f1000prime data"
    - "scholarly twitter metrics"
    - "social media metrics for new research evaluation"
    - "automated analysis of topic actor networks on twitter new approach to the analysis of socio semantic networks"
    - "climate change research in view of bibliometrics"
    - "scholarly use of social media and altmetrics a review of the literature"
    - "national research impact indicators from mendeley readers"
    - "tweets vs mendeley readers how do these two social media metrics differ"

Connected through hyperedge HE-3 (induced by source paper "do altmetrics correlate with the quality of papers a large scale empirical study based on f1000prime data"):
  with members:
    - "allegation of scientific misconduct increases twitter attention"
    - "grand challenges in altmetrics heterogeneity data quality and dependencies"
    - "scholarly use of social media and altmetrics a review of the literature"
    - "social media in scholarly communication"
    - "validity of altmetrics data for measuring societal impact a study using data from altmetric and f1000prime"
    - "regression for citation data an evaluation of different methods"
    - "do altmetrics correlate with citations extensive comparison of altmetric indicators with citations from a multidisciplinary perspective"
    - "alternative metrics in scientometrics a meta analysis of research into three altmetrics"

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)