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: "when politicians talk assessing online conversational practices of political parties on twitter"
  Abstract: Assessing political conversations in social media requires a deeper understanding of the underlying practices and styles that drive these conversations. In this paper, we present a computational approach for assessing online conversational practices of political parties. Following a deductive approach, we devise a number of quantitative measures from a discussion of theoretical constructs in sociological theory. The resulting measures make different - mostly qualitative - aspects of online conversational practices amenable to computation. We evaluate our computational approach by applying it in a case study. In particular, we study online conversational practices of German politicians on Twitter during the German federal election 2013. We find that political parties share some interesting patterns of behavior, but also exhibit some unique and interesting idiosyncrasies. Our work sheds light on (i) how complex cultural phenomena such as online conversational practices are amenable to quantification and (ii) the way social media such as Twitter are utilized by political parties.

Connected through hyperedge HE-1 (induced by source paper "algorithmic distortion of informational landscapes"):
  with members:
    - "the spread of low credibility content by social bots"
    - "evolution of ego networks in social media with link recommendations"
    - "the rise of social bots"
    - "people are strange when you re a stranger impact and influence of bots on social networks"

Connected through hyperedge HE-2 (induced by source paper "who falls for online political manipulation"):
  with members:
    - "arming the public with artificial intelligence to counter social bots"
    - "analyzing the digital traces of political manipulation the 2016 russian interference twitter campaign"
    - "disinformation and social bot operations in the run up to the 2017 french presidential election"
    - "early detection of promoted campaigns on social media"
    - "the paradigm shift of social spambots evidence theories and tools for the arms race"
    - "botornot a system to evaluate social bots"
    - "the darpa twitter bot challenge"
    - "the rise of social bots"

Connected through hyperedge HE-3 (induced by source paper "ilcm a virtual research infrastructure for large scale qualitative data"):
  with members:
    - "election campaigning on social media politicians audiences and the mediation of political communication on facebook and twitter"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "election campaigning on social media politicians audiences and the mediation of political communication on facebook and twitter"):
  with members:
    - "social media monitoring of the campaigns for the 2013 german bundestag elections on facebook and twitter"

Connected through hyperedge HE-5 (induced by source paper "think before you collect setting up a data collection approach for social media studies"):
  with members:
    - "tweets vs mendeley readers how do these two social media metrics differ"
    - "evolution of reddit from the front page of the internet to a self referential community"
    - "when politicians tweet a study on the members of the german federal diet"

Connected through hyperedge HE-6 (induced by source paper "voting behaviour and power in online democracy a study of liquidfeedback in germany s pirate party"):
  with members:
    - "is the crowd s wisdom biased a quantitative analysis of three online communities"

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.SI(Social and Information Networks)