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: "election campaigning on social media politicians audiences and the mediation of political communication on facebook and twitter"
  Abstract: Although considerable research has concentrated on online campaigning, it is still unclear how politicians use different social media platforms in political communication. Focusing on the German federal election campaign 2013, this article investigates whether election candidates address the topics most important to the mass audience and to which extent their communication is shaped by the characteristics of Facebook and Twitter. Based on open-ended responses from a representative survey conducted during the election campaign, we train a human-interpretable Bayesian language model to identify political topics. Applying the model to social media messages of candidates and their direct audiences, we find that both prioritize different topics than the mass audience. The analysis also shows that politicians use Facebook and Twitter for different purposes. We relate the various findings to the mediation of political communication on social media induced by the particular characteristics of audiences and sociotechnical environments.

Connected through hyperedge HE-1 (induced by source paper "political elections under social fire analysis and detection of propaganda on twitter"):
  with members:
    - "influence of fake news in twitter during the 2016 us presidential election"
    - "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"
    - "deep neural networks for bot detection"
    - "the paradigm shift of social spambots evidence theories and tools for the arms race"
    - "online human bot interactions detection estimation and characterization"
    - "botornot a system to evaluate social bots"
    - "measuring user influence on twitter"

Connected through hyperedge HE-2 (induced by source paper "election control with voters uncertainty hardness and approximation results"):
  with members:
    - "vote for me election control via social influence in arbitrary scoring rule voting systems"
    - "election with bribed voter uncertainty hardness and approximation algorithm"
    - "disinformation and social bot operations in the run up to the 2017 french presidential election"
    - "controlling elections through social influence"
    - "almost polynomial ratio eth hardness of approximating densest k subgraph"
    - "complexity of shift bribery in committee elections"
    - "minority becomes majority in social networks"
    - "determining possible and necessary winners under common voting rules given partial orders"

Connected through hyperedge HE-3 (induced by source paper "ilcm a virtual research infrastructure for large scale qualitative data"):
  with members:
    - "detecting and assessing contextual change in diachronic text documents using context volatility"
    - "a tidy data model for natural language processing using cleannlp"
    - "the polylingual labeled topic model"
    - "when politicians talk assessing online conversational practices of political parties 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)