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 "supporting early and scalable discovery of disinformation websites"):
  Abstract: Online disinformation is a serious and growing sociotechnical problem that threatens the integrity of public discourse, democratic governance, and commerce. The internet has made it easier than ever to spread false information, and academic research is just beginning to comprehend the consequences. In response to this growing problem, online services have established processes to counter disinformation. These processes predominantly rely on costly and painstaking manual analysis, however, often responding to disinformation long after it has spread. We design, develop, and evaluate a new approach for proactively discovering disinformation websites. Our approach is inspired by the information security literature on identifying malware distribution, phishing, and scam websites using distinctive non-perceptual infrastructure characteristics. We show that automated identification with similar features can effectively support human judgments for early and scalable discovery of disinformation websites. Our system significantly exceeds the state of the art in detecting disinformation websites, and we present the first reported real-time evaluation of automation-supported disinformation discovery. We also demonstrate, as a proof of concept, how our approach could be easily operationalized in ordinary consumer web browsers.

Includes members:
  Member "a retrospective analysis of the fake news challenge stance detection task":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "stance detection benchmark how robust is your stance detection")
  Member "endorsements on social media an empirical study of affiliate marketing disclosures on youtube and pinterest":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "dark patterns at scale findings from a crawl of 11k shopping websites")
  Member "fakenewsnet a data repository with news content social context and dynamic information for studying fake news on social media":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "discover your social identity from what you tweet a content based approach")
  Member "fever a large scale dataset for fact extraction and verification":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the microsoft toolkit of multi task deep neural networks for natural language understanding")
  Member "trends in the diffusion of misinformation on social media":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "como mensurar a import ancia influ encia e a relev ancia de usu arios do twitter uma an alise da intera c c ao dos candidatos a presid encia do brasil nas elei c c oes de 2018")
  Member "fake news detection on social media a data mining perspective":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "benchmark performance of machine and deep learning based methodologies for urdu text document classification")
  Member "analysing how people orient to and spread rumours in social media by looking at conversational threads":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "two path deep semi supervised learning for timely fake news detection")
  Member "computational fact checking from knowledge networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "knowledge graphs")

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)