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 "polarization and fake news early warning of potential misinformation targets"):
  Abstract: Users polarization and confirmation bias play a key role in misinformation spreading on online social media. Our aim is to use this information to determine in advance potential targets for hoaxes and fake news. In this paper, we introduce a general framework for promptly identifying polarizing content on social media and, thus, "predicting" future fake news topics. We validate the performances of the proposed methodology on a massive Italian Facebook dataset, showing that we are able to identify topics that are susceptible to misinformation with 77% accuracy. Moreover, such information may be embedded as a new feature in an additional classifier able to recognize fake news with 91% accuracy. The novelty of our approach consists in taking into account a series of characteristics related to users behavior on online social media, making a first, important step towards the smoothing of polarization and the mitigation of misinformation phenomena.

Includes members:
  Member "polarization of the vaccination debate on facebook":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the covid 19 social media infodemic")
  Member "misinformation spreading on facebook":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "poprank ranking pages impact and users engagement on facebook")
  Member "it s always april fools day on the difficulty of social network misinformation classification via propagation features":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "towards understanding the information ecosystem through the lens of multiple web communities")
  Member "leveraging the crowd to detect and reduce the spread of fake news and misinformation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the future of misinformation detection new perspectives and trends")
  Member "fake news detection on social media a data mining perspective":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "benchmark performance of machine and deep learning based methodologies for urdu text document classification")
  Member "automatically identifying fake news in popular twitter threads":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "towards understanding the information ecosystem through the lens of multiple web communities")
  Member "tweet acts a speech act classifier for twitter":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "target based speech act classification in political campaign text")
  Member "debunking in a world of tribes":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "the covid 19 social media infodemic")

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)