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: "measuring field normalized impact of papers on specific societal groups an altmetrics study based on mendeley data"
  Abstract: Bibliometrics is successful in measuring impact because the target is clearly defined: the publishing scientist who is still active and working. Thus, citations are a target-oriented metric which measures impact on science. In contrast, societal impact measurements based on altmetrics are as a rule intended to measure impact in a broad sense on all areas of society (e.g. science, culture, politics, and economics). This tendency is especially reflected in the efforts to design composite indicators (e.g. the Altmetric Attention Score). We deem appropriate that not only the impact measurement using citations is target-oriented (citations measure the impact of papers on scientists) but also the measurement of impact using altmetrics. Impact measurements only make sense, if the target group—the recipient of academic papers—is clearly defined. Thus, we extend in this study the field-normalized reader impact indicator proposed by us in an earlier study, which is based on Mendeley data (the mean normalized reader score, MNRS), to a target-oriented field-normalized impact indicator (e.g. MNRSED measures reader impact on the sector of educational donation, i.e. teaching). This indicator can show—as demonstrated in empirical examples—the ability of journals, countries, and academic institutions to publish papers which are below or above the average impact of papers on a specific sector in society (e.g. the educational or teaching sector). Thus, the method allows to measure the impact of scientific papers on certain groups—controlling for the field in which the papers have been published and their publication year.

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"
    - "normalization of zero inflated data an empirical analysis of a new indicator family and its use with altmetrics 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 "field and time normalization of data with many zeros an empirical analysis using citation and twitter data"):
  with members:
    - "normalization of zero inflated data an empirical analysis of a new indicator family"
    - "three practical field normalised alternative indicator formulae for research evaluation"
    - "grand challenges in altmetrics heterogeneity data quality and dependencies"
    - "national research impact indicators from mendeley readers"
    - "validity of altmetrics data for measuring societal impact a study using data from altmetric and f1000prime"
    - "tweets vs mendeley readers how do these two social media metrics differ"
    - "investigation of partition cells as a structural basis suitable for assessments of individual scientists"
    - "partition based field normalization an approach to highly specialized publication records"

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)