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: "on the relationships between bibliographic characteristics of scientific documents and citation and mendeley readership counts a large scale analysis of web of science publications"
  Abstract: In this paper we present a first large-scale analysis of the relationship between Mendeley readership and citation counts with particular documents’ bibliographic characteristics. A data set of 1.3 million publications from different fields published in journals covered by the Web of Science (WoS) has been analyzed. This work reveals that document types that are often excluded from citation analysis due to their lower citation values, like editorial materials, letters, news items, or meeting abstracts, are strongly covered and saved in Mendeley, suggesting that Mendeley readership can reliably inform the analysis of these document types. Findings show that collaborative papers are frequently saved in Mendeley, which is similar to what is observed for citations. The relationship between readership and the length of titles and number of pages, however, is weaker than for the same relationship observed for citations. The analysis of different disciplines also points to different patterns in the relationship between several document characteristics, readership, and citation counts. Overall, results highlight that although disciplinary differences exist, readership counts are related to similar bibliographic characteristics as those related to citation counts, reinforcing the idea that Mendeley readership and citations capture a similar concept of impact, although they cannot be considered as equivalent indicators.

Connected through hyperedge HE-1 (induced by source paper "mendeley reader counts for us computer science conference papers and journal articles"):
  with members:
    - "google scholar web of science and scopus a systematic comparison of citations in 252 subject categories"
    - "could scientists use altmetric com scores to predict longer term citation counts"
    - "which type of citation analysis generates the most accurate taxonomy of scientific and technical knowledge"
    - "mendeley readership as a filtering tool to identify highly cited publications"
    - "role of interdisciplinarity in computer sciences quantification impact and life trajectory"
    - "are the discretised lognormal and hooked power law distributions plausible for citation data"
    - "more precise methods for national research citation impact comparisons"
    - "tweets vs mendeley readers how do these two social media metrics differ"

Connected through hyperedge HE-2 (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"
    - "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"
    - "the influence of time and discipline on the magnitude of correlations between citation counts and quality scores"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "social media metrics for new research evaluation"):
  with members:
    - "towards the social media studies of science social media metrics present and future"
    - "mendeley readership as a filtering tool to identify highly cited publications"
    - "scholars on twitter who and how many are they"
    - "three practical field normalised alternative indicator formulae for research evaluation"
    - "a systematic identification and analysis of scientists on twitter"
    - "grand challenges in altmetrics heterogeneity data quality and dependencies"
    - "t factor a metric for measuring impact on twitter"
    - "iran s scientific dominance and the emergence of south east asian countries as scientific collaborators in the persian gulf region"

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)