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: "the fake news spreading plague was it preventable"
  Abstract: In 2010, a paper entitled "From Obscurity to Prominence in Minutes: Political Speech and Real-time search" won the Best Paper Prize of the Web Science 2010 Conference. Among its findings were the discovery and documentation of what was termed a "Twitter-bomb", an organized effort to spread misinformation about the democratic candidate Martha Coakley through anonymous Twitter accounts. In this paper, after summarizing the details of that event, we outline the recipe of how social networks are used to spread misinformation. One of the most important steps in such a recipe is the "infiltration" of a community of users who are already engaged in conversations about a topic, to use them as organic spreaders of misinformation in their extended subnetworks. Then, we take this misinformation spreading recipe and indicate how it was successfully used to spread fake news during the 2016 U.S. Presidential Election. The main differences between the scenarios are the use of Facebook instead of Twitter, and the respective motivations (in 2010: political influence; in 2016: financial benefit through online advertising). After situating these events in the broader context of exploiting the Web, we seize this opportunity to address limitations of the reach of research findings and to start a conversation about how communities of researchers can increase their impact on real-world societal issues.

Connected through hyperedge HE-1 (induced by source paper "deception strategies and threats for online discussions"):
  with members:
    - "influence of fake news in twitter during the 2016 us presidential election"
    - "anatomy of an online misinformation network"
    - "the spread of low credibility content by social bots"
    - "soc2seq social embedding meets conversation model"
    - "reuters tracer toward automated news production using large scale social media data"
    - "early detection of promoted campaigns on social media"
    - "discovery retrieval and analysis of star wars botnet in twitter"
    - "limited individual attention and online virality of low quality information"

Connected through hyperedge HE-2 (induced by source paper "spread of hate speech in online social media"):
  with members:
    - "what is gab a bastion of free speech or an alt right echo chamber"
    - "a quantitative approach to understanding online antisemitism"
    - "the effect of extremist violence on hateful speech online"
    - "inside the right leaning echo chambers characterizing gab an unmoderated social system"
    - "hierarchical cvae for fine grained hate speech classification"
    - "thou shalt not hate countering online hate speech"
    - "hateminers detecting hate speech against women"
    - "analyzing right wing youtube channels hate violence and discrimination"

Connected through hyperedge HE-3 (induced by source paper "trust based dynamic linear threshold models for non competitive and competitive influence propagation"):
  with members:
    - "an efficient randomized algorithm for rumor blocking in online social networks"
    - "from competition to complementarity comparative influence diffusion and maximization"
    - "modeling non progressive phenomena for influence propagation"
    - "viral misinformation the role of homophily and polarization"

Connected through hyperedge HE-4 (induced by source paper "fake news detection on social media a data mining perspective"):
  with members:
    - "detection and resolution of rumours in social media a survey"
    - "liar liar pants on fire a new benchmark dataset for fake news detection"
    - "fake news mitigation via point process based intervention"
    - "a stylometric inquiry into hyperpartisan and fake news"
    - "anyone can become a troll causes of trolling behavior in online discussions"
    - "csi a hybrid deep model for fake news detection"
    - "some like it hoax automated fake news detection in social networks"
    - "hoaxy a platform for tracking online misinformation"

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)