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 "debunking in a world of tribes"):
  Abstract: Recently a simple military exercise on the Internet was perceived as the beginning of a new civil war in the US. Social media aggregate people around common interests eliciting a collective framing of narratives and worldviews. However, the wide availability of user-provided content and the direct path between producers and consumers of information often foster confusion about causations, encouraging mistrust, rumors, and even conspiracy thinking. In order to contrast such a trend attempts to \textit{debunk} are often undertaken. Here, we examine the effectiveness of debunking through a quantitative analysis of 54 million users over a time span of five years (Jan 2010, Dec 2014). In particular, we compare how users interact with proven (scientific) and unsubstantiated (conspiracy-like) information on Facebook in the US. Our findings confirm the existence of echo chambers where users interact primarily with either conspiracy-like or scientific pages. Both groups interact similarly with the information within their echo chamber. We examine 47,780 debunking posts and find that attempts at debunking are largely ineffective. For one, only a small fraction of usual consumers of unsubstantiated information interact with the posts. Furthermore, we show that those few are often the most committed conspiracy users and rather than internalizing debunking information, they often react to it negatively. Indeed, after interacting with debunking posts, users retain, or even increase, their engagement within the conspiracy echo chamber.

Includes members:
  Member "multilingual twitter sentiment classification the role of human annotators":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "garbage in garbage out do machine learning application papers in social computing report where human labeled training data comes from")
  Member "trend of narratives in the age of misinformation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "influence of fake news in twitter during the 2016 us presidential election")
  Member "science vs conspiracy collective narratives in the age of misinformation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "the covid 19 social media infodemic")
  Member "emotional dynamics in the age of misinformation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "towards understanding the information ecosystem through the lens of multiple web communities")
  Member "computational fact checking from knowledge networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "knowledge graphs")
  Member "echo chambers in the age of misinformation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "information credibility in the social web contexts approaches and open issues")
  Member "knowledge based trust estimating the trustworthiness of web sources":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "detecting toxicity in news articles application to bulgarian")
  Member "collective attention in the age of mis information":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "computational human dynamics")

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.CY(Computers and Society)