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: "polarization partisanship and junk news consumption over social media in the us"
  Abstract: What kinds of social media users read junk news? We examine the distribution of the most significant sources of junk news in the three months before President Donald Trump first State of the Union Address. Drawing on a list of sources that consistently publish political news and information that is extremist, sensationalist, conspiratorial, masked commentary, fake news and other forms of junk news, we find that the distribution of such content is unevenly spread across the ideological spectrum. We demonstrate that (1) on Twitter, a network of Trump supporters shares the widest range of known junk news sources and circulates more junk news than all the other groups put together; (2) on Facebook, extreme hard right pages, distinct from Republican pages, share the widest range of known junk news sources and circulate more junk news than all the other audiences put together; (3) on average, the audiences for junk news on Twitter share a wider range of known junk news sources than audiences on Facebook public pages.

Connected through hyperedge HE-1 (induced by source paper "intelligent social bots uncover the link between user preference and diversity of news consumption"):
  with members:
    - "bag of tricks for efficient text classification"
    - "biased assimilation homophily and the dynamics of polarization"
    - "solving the apparent diversity accuracy dilemma of recommender systems"

Connected through hyperedge HE-2 (induced by source paper "distributionally robust removal of malicious nodes from networks"):
  with members:
    - "removing malicious nodes from networks"
    - "automated hate speech detection and the problem of offensive language"
    - "antisocial behavior in online discussion communities"

Connected through hyperedge HE-3 (induced by source paper "the reach of commercially motivated junk news on facebook"):
  with members:
    - "trends in the diffusion of misinformation on social media"

Connected through hyperedge HE-4 (induced by source paper "removing malicious nodes from networks"):
  with members:
    - "automated hate speech detection and the problem of offensive language"
    - "antisocial behavior in online discussion communities"

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)