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: "could scientists use altmetric com scores to predict longer term citation counts"
  Abstract: Abstract   Altmetrics from Altmetric.com are widely used by publishers and researchers to give earlier evidence of attention than citation counts. This article assesses whether Altmetric.com scores are reliable early indicators of likely future impact and whether they may also reflect non-scholarly impacts. A preliminary factor analysis suggests that the main altmetric indicator of scholarly impact is Mendeley reader counts, with weaker news, informational and social network discussion/promotion dimensions in some fields. Based on a regression analysis of Altmetric.com data from November 2015 and Scopus citation counts from October 2017 for articles in 30 narrow fields, only Mendeley reader counts are consistent predictors of future citation impact. Most other Altmetric.com scores can help predict future impact in some fields. Overall, the results confirm that early Altmetric.com scores can predict later citation counts, although less well than journal impact factors, and the optimal strategy is to consider both Altmetric.com scores and journal impact factors. Altmetric.com scores can also reflect dimensions of non-scholarly impact in some fields.

Connected through hyperedge HE-1 (induced by source paper "measuring the diversity of facebook reactions to research"):
  with members:
    - "jointly learning to detect emotions and predict facebook reactions"
    - "shared feelings understanding facebook reactions to scholarly articles"
    - "community interaction and conflict on the web"
    - "social emotion mining techniques for facebook posts reaction prediction"
    - "anatomy of scholarly information behavior patterns in the wake of academic social media platforms"
    - "scholarly use of social media and altmetrics a review of the literature"
    - "140 characters to victory using twitter to predict the uk 2015 general election"
    - "anger is more influential than joy sentiment correlation in weibo"

Connected through hyperedge HE-2 (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"
    - "on the relationships between bibliographic characteristics of scientific documents and citation and mendeley readership counts a large scale analysis of web of science publications"
    - "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-3 (induced by source paper "how much research output from india gets social media attention"):
  with members:
    - "which us and european higher education institutions are visible in researchgate and what affects their rg score"
    - "the poor altmetric performance of publications authored by researchers in mainland china"
    - "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"
    - "tweeting biomedicine an analysis of tweets and citations in the biomedical literature"

Connected through hyperedge HE-4 (induced by source paper "disciplinary variations in altmetric coverage of scholarly articles"):
  with members:
    - "the poor altmetric performance of publications authored by researchers in mainland china"
    - "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"
    - "tweeting biomedicine an analysis of tweets and citations in the biomedical literature"

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)