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: "the wisdom of polarized crowds"
  Abstract: As political polarization in the United States continues to rise1–3, the question of whether polarized individuals can fruitfully cooperate becomes pressing. Although diverse perspectives typically lead to superior team performance on complex tasks4,5, strong political perspectives have been associated with conflict, misinformation and a reluctance to engage with people and ideas beyond one’s echo chamber6–8. Here, we explore the effect of ideological composition on team performance by analysing millions of edits to Wikipedia’s political, social issues and science articles. We measure editors’ online ideological preferences by how much they contribute to conservative versus liberal articles. Editor surveys suggest that online contributions associate with offline political party affiliation and ideological self-identity. Our analysis reveals that polarized teams consisting of a balanced set of ideologically diverse editors produce articles of a higher quality than homogeneous teams. The effect is most clearly seen in Wikipedia’s political articles, but also in social issues and even science articles. Analysis of article ‘talk pages’ reveals that ideologically polarized teams engage in longer, more constructive, competitive and substantively focused but linguistically diverse debates than teams of ideological moderates. More intense use of Wikipedia policies by ideologically diverse teams suggests institutional design principles to help unleash the power of polarization.

Connected through hyperedge HE-1 (induced by source paper "social centralization and semantic collapse hyperbolic embeddings of networks and text"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "word embeddings quantify 100 years of gender and ethnic stereotypes"
    - "poincar e embeddings for learning hierarchical representations"
    - "man is to computer programmer as woman is to homemaker debiasing word embeddings"
    - "network mapping by replaying hyperbolic growth"
    - "deepwalk online learning of social representations"
    - "distributed representations of words and phrases and their compositionality"
    - "echoes of power language effects and power differences in social interaction"

Connected through hyperedge HE-2 (induced by source paper "online disinformation and the role of wikipedia"):
  with members:
    - "citation needed a taxonomy and algorithmic assessment of wikipedia s verifiability"
    - "auditing radicalization pathways on youtube"
    - "the fact extraction and verification fever shared task"
    - "exposing deepfake videos by detecting face warping artifacts"
    - "fake news a survey of research detection methods and opportunities"
    - "fever a large scale dataset for fact extraction and verification"
    - "strategies and influence of social bots in a 2017 german state election a case study on twitter"
    - "do social bots dream of electric sheep a categorisation of social media bot accounts"

Connected through hyperedge HE-3 (induced by source paper "maximizing contrasting opinions in signed social networks"):
  with members:
    - "minimizing polarization and disagreement in social networks"
    - "reducing controversy by connecting opposing views"
    - "a survey of signed network mining in social media"
    - "from competition to complementarity comparative influence diffusion and maximization"
    - "influence diffusion dynamics and influence maximization in social networks with friend and foe relationships"

Connected through hyperedge HE-4 (induced by source paper "characterizing and detecting hateful users on twitter"):
  with members:
    - "understanding abuse a typology of abusive language detection subtasks"
    - "mean birds detecting aggression and bullying on twitter"
    - "automated hate speech detection and the problem of offensive language"
    - "inductive representation learning on large graphs"
    - "deep learning for hate speech detection in tweets"
    - "representation learning on graphs methods and applications"
    - "detecting the hate code on social media"
    - "hate is not binary studying abusive behavior of gamergate 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.SI(Social and Information Networks)