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: "partisanship propaganda and post truth politics quantifying impact in online debate"
  Abstract: The recent past has highlighted the influential role of social networks and online media in shaping public debate on current affairs and political issues. This paper is focused on studying the role of politically-motivated actors and their strategies for influencing and manipulating public opinion online: partisan media, state-backed propaganda, and post-truth politics. In particular, we present quantitative research on the presence and impact of these three `Ps' in online Twitter debates in two contexts: (i) the run up to the UK EU membership referendum (`Brexit'); and (ii) the information operations of Russia-backed online troll accounts. We first compare the impact of highly partisan versus mainstream media during the Brexit referendum, specifically comparing tweets by half a million `leave' and `remain' supporters. Next, online propaganda strategies are examined, specifically left- and right-wing troll accounts. Lastly, we study the impact of misleading claims made by the political leaders of the leave and remain campaigns. This is then compared to the impact of the Russia-backed partisan media and propaganda accounts during the referendum. In particular, just two of the many misleading claims made by politicians during the referendum were found to be cited in 4.6 times more tweets than the 7,103 tweets related to Russia Today and Sputnik and in 10.2 times more tweets than the 3,200 Brexit-related tweets by the Russian troll accounts.

Connected through hyperedge HE-1 (induced by source paper "race and religion in online abuse towards uk politicians working paper"):
  with members:
    - "online abuse of uk mps from 2015 to 2019 working paper"
    - "tweeting mps digital engagement between citizens and members of parliament in the uk"
    - "lipstick on a pig debiasing methods cover up systematic gender biases in word embeddings but do not remove them"
    - "adversarial removal of demographic attributes from text data"
    - "reducing gender bias in abusive language detection"
    - "gender bias in coreference resolution evaluation and debiasing methods"
    - "semantics derived automatically from language corpora contain human like biases"
    - "equality of opportunity in supervised learning"

Connected through hyperedge HE-2 (induced by source paper "analysing russian trolls via nlp tools"):
  with members:
    - "tracking the digital traces of russian trolls distinguishing the roles and strategy of trolls on twitter"
    - "who let the trolls out towards understanding state sponsored trolls"
    - "analyzing the digital traces of political manipulation the 2016 russian interference twitter campaign"
    - "mobilizing the trump train understanding collective action in a political trolling community"
    - "the spread of low credibility content by social bots"
    - "an army of me sockpuppets in online discussion communities"
    - "the spread of fake news by social bots"
    - "botornot a system to evaluate social bots"

Connected through hyperedge HE-3 (induced by source paper "textrolls identifying russian trolls on twitter from a textual perspective"):
  with members:
    - "still out there modeling and identifying russian troll accounts on twitter"
    - "disinformation warfare understanding state sponsored trolls on twitter and their influence on the web"
    - "analyzing the digital traces of political manipulation the 2016 russian interference twitter campaign"
    - "who falls for online political manipulation"
    - "tweet2vec character based distributed representations for social media"
    - "sifting robotic from organic text a natural language approach for detecting automation on twitter"

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.CY(Computers and Society)