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: "partisan sharing facebook evidence and societal consequences"
  Abstract: The hypothesis of selective exposure assumes that people seek out information that supports their views and eschew information that conflicts with their beliefs, and that has negative consequences on our society. Few researchers have recently found counter evidence of selective exposure in social media: users are exposed to politically diverse articles. No work has looked at what happens after exposure, particularly how individuals react to such exposure, though. Users might well be exposed to diverse articles but share only the partisan ones. To test this, we study partisan sharing on Facebook: the tendency for users to predominantly share like-minded news articles and avoid conflicting ones. We verified four main hypotheses. That is, whether partisan sharing: 1) exists at all; 2) changes across individuals (e.g., depending on their interest in politics); 3) changes over time (e.g., around elections); and 4) changes depending on perceived importance of topics. We indeed find strong evidence for partisan sharing. To test whether it has any consequence in the real world, we built a web application for BBC viewers of a popular political program, resulting in a controlled experiment involving more than 70 individuals. Based on what they share and on survey data, we find that partisan sharing has negative consequences: distorted perception of reality. However, we do also find positive aspects of partisan sharing: it is associated with people who are more knowledgeable about politics and engage more with it as they are more likely to vote in the general elections.

Connected through hyperedge HE-1 (induced by source paper "political discussions in homogeneous and cross cutting communication spaces"):
  with members:
    - "caveat emptor computational social science large scale missing data in a widely published reddit corpus"
    - "winning arguments interaction dynamics and persuasion strategies in good faith online discussions"
    - "diachronic word embeddings reveal statistical laws of semantic change"
    - "exploring cyberbullying and other toxic behavior in team competition online games"
    - "efficient estimation of word representations in vector space"
    - "partisan asymmetries in online political activity"

Connected through hyperedge HE-2 (induced by source paper "political discourse on social media echo chambers gatekeepers and the price of bipartisanship"):
  with members:
    - "balancing information exposure in social networks"
    - "the effect of collective attention on controversial debates on social media"
    - "a long term analysis of polarization on twitter"
    - "limited individual attention and online virality of low quality information"
    - "reducing controversy by connecting opposing views"
    - "quantifying controversy in social media"

Connected through hyperedge HE-3 (induced by source paper "identifying partisan slant in news articles and twitter during political crises"):
  with members:
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-4 (induced by source paper "quantifying controversy in social media"):
  with members:
    - "characterizing information diets of social media users"
    - "controversy and sentiment in online news"
    - "social determinants of content selection in the age of mis information"
    - "data portraits connecting people of opposing views"
    - "efficient community detection in large networks using content and links"
    - "modularity clustering is force directed layout"

Connected through hyperedge HE-5 (induced by source paper "hyptrails a bayesian approach for comparing hypotheses about human trails on the web"):
  with members:
    - "discovering beaten paths in collaborative ontology engineering projects using markov chains"
    - "detecting memory and structure in human navigation patterns using markov chain models of varying order"

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)