human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "social media news and political information during the us election was polarizing content concentrated in swing states"):
  Abstract: US voters shared large volumes of polarizing political news and information in the form of links to content from Russian, WikiLeaks and junk news sources. Was this low quality political information distributed evenly around the country, or concentrated in swing states and particular parts of the country? In this data memo we apply a tested dictionary of sources about political news and information being shared over Twitter over a ten day period around the 2016 Presidential Election. Using self-reported location information, we place a third of users by state and create a simple index for the distribution of polarizing content around the country. We find that (1) nationally, Twitter users got more misinformation, polarizing and conspiratorial content than professionally produced news. (2) Users in some states, however, shared more polarizing political news and information than users in other states. (3) Average levels of misinformation were higher in swing states than in uncontested states, even when weighted for the relative size of the user population in each state. We conclude with some observations about the impact of strategically disseminated polarizing information on public life.

Includes members:
  Member "on predicting geolocation of tweets using convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "discover your social identity from what you tweet a content based approach")
  Member "a survey of location inference techniques on twitter":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "location reference identification from tweets during emergencies a deep learning approach")

Question: Please tell me which class the source paper 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)