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: "scholarly twitter metrics"
  Abstract: Twitter has arguably been the most popular among the data sources that form the basis of so-called altmetrics. Tweets to scholarly documents have been heralded as both early indicators of citations as well as measures of societal impact. This chapter provides an overview of Twitter activity as the basis for scholarly metrics from a critical point of view and equally describes the potential and limitations of scholarly Twitter metrics. By reviewing the literature on Twitter in scholarly communication and analyzing 24 million tweets linking to scholarly documents, it aims to provide a basic understanding of what tweets can and cannot measure in the context of research evaluation. Going beyond the limited explanatory power of low correlations between tweets and citations, this chapter considers what types of scholarly documents are popular on Twitter, and how, when and by whom they are diffused in order to understand what tweets to scholarly documents measure. Although this chapter is not able to solve the problems associated with the creation of meaningful metrics from social media, it highlights particular issues and aims to provide the basis for advanced scholarly Twitter metrics.

Connected through hyperedge HE-1 (induced by source paper "the stability of twitter metrics a study on unavailable twitter mentions of scientific publications"):
  with members:
    - "social media metrics for new research evaluation"
    - "mendeley readership as a filtering tool to identify highly cited publications"
    - "grand challenges in altmetrics heterogeneity data quality and dependencies"
    - "using almetrics for contextualised mapping of societal impact from hits to networks"
    - "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"
    - "tweets as impact indicators examining the implications of automated bot accounts on twitter"
    - "do altmetrics point to the broader impact of research an overview of benefits and disadvantages of altmetrics"

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"
    - "social media metrics for new research evaluation"
    - "automated analysis of topic actor networks on twitter new approach to the analysis of socio semantic networks"
    - "normalization of zero inflated data an empirical analysis of a new indicator family and its use with altmetrics data"
    - "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"

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.SI(Social and Information Networks)