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: "automated analysis of topic actor networks on twitter new approach to the analysis of socio semantic networks"
  Abstract: Social-media data provides increasing opportunities for automated analysis of large sets of textual documents. So far, automated tools have been developed to account for either the social networks between the participants of the debates, or to analyze the content of those debates. Less attention has been paid to mapping co-occurring actors (participants) and topics (content) in online debates that form socio-semantic networks. We propose a new, automated approach that uses a whole matrix approach of co-addressed topics and the actors. We show the advantages of the new approach with the analysis of a large set of English-language Twitter messages at the Rio+20 meeting, in June 2012 (72,077 tweets), and a smaller data set of Dutch-language Twitter messages on bird flu related to poultry farming in 2015-2017 (2,139 tweets). We discuss the theoretical, methodological and substantive implications of our approach, also for the analysis of other social-media data.

Connected through hyperedge HE-1 (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"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "foundations of temporal text networks"):
  with members:
    - "detecting socio economic impact of cultural investment through geo social network analysis"
    - "graph embedding techniques applications and performance a survey"
    - "motifs in temporal networks"
    - "when is a network a network multi order graphical model selection in pathways and temporal networks"
    - "modelling sequences and temporal networks with dynamic community structures"
    - "do cascades recur"
    - "clustering attributed graphs models measures and methods"
    - "spreading processes in multilayer networks"

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)