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 "a survey on computational politics"):
  Abstract: Computational Politics is the study of computational methods to analyze and moderate users\textquotesingle behaviors related to political activities such as election campaign persuasion, political affiliation, and opinion mining. With the rapid development and ease of access to the Internet, Information Communication Technologies (ICT) have given rise to a massive number of users joining the online communities and to the digitization of analogous data such as political debates. These communities and digitized data contain both explicit and latent information about users and their behaviors related to politics. For researchers, it is essential to utilize data from these sources to develop and design systems that not only provide solutions to computational politics but also help other businesses, such as marketers, to increase the users\textquotesingle participation and interaction. In this survey, we attempt to categorize main areas in computational politics and summarize the prominent studies at one place to better understand computational politics across different and multidimensional platforms. e.g., online social networks, online forums, and political debates. We then conclude this study by highlighting future research directions, opportunities, and challenges.

Includes members:
  Member "studying fake news via network analysis detection and mitigation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "mining disinformation and fake news concepts methods and recent advancements")
  Member "tweeting mps digital engagement between citizens and members of parliament in the uk":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "stop tracking me bro differential tracking of user demographics on hyper partisan websites")
  Member "political discussions in homogeneous and cross cutting communication spaces":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "the pushshift reddit dataset")
  Member "political discourse on social media echo chambers gatekeepers and the price of bipartisanship":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "facebook ads monitor an independent auditing system for political ads on facebook")
  Member "quantifying search bias investigating sources of bias for political searches in social media":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "mining disinformation and fake news concepts methods and recent advancements")
  Member "election bias comparing polls and twitter in the 2016 u s election":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "measuring relative opinion from location based social media a case study of the 2016 u s presidential election")
  Member "leveraging the crowd to detect and reduce the spread of fake news and misinformation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "the future of misinformation detection new perspectives and trends")
  Member "mining the demographics of political sentiment from twitter using learning from label proportions":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "transfer learning based label proportions method with data of uncertainty")

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)