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: "biased assimilation homophily and the dynamics of polarization"
  Abstract: Are we as a society getting more polarized, and if so, why? We try to answer this question through a model of opinion formation. Empirical studies have shown that homophily results in polarization. However, we show that DeGroot's well-known model of opinion formation based on repeated averaging can never be polarizing, even if individuals are arbitrarily homophilous. We generalize DeGroot's model to account for a phenomenon well-known in social psychology as biased assimilation: when presented with mixed or inconclusive evidence on a complex issue, individuals draw undue support for their initial position thereby arriving at a more extreme opinion. We show that in a simple model of homophilous networks, our biased opinion formation process results in either polarization, persistent disagreement or consensus depending on how biased individuals are. In other words, homophily alone, without biased assimilation, is not sufficient to polarize society. Quite interestingly, biased assimilation also provides insight into the following related question: do internet based recommender algorithms that show us personalized content contribute to polarization? We make a connection between biased assimilation and the polarizing effects of some random-walk based recommender algorithms that are similar in spirit to some commonly used recommender algorithms.

Connected through hyperedge HE-1 (induced by source paper "vocabulary based method for quantifying controversy in social media"):
  with members:
    - "quantifying polarization on twitter the kavanaugh nomination"
    - "community interaction and conflict on the web"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "joint non negative matrix factorization for learning ideological leaning on twitter"
    - "a distance measure for the analysis of polar opinion dynamics in social networks"
    - "characterizing information diets of social media users"
    - "using word embeddings in twitter election classification"
    - "reducing controversy by connecting opposing views"

Connected through hyperedge HE-2 (induced by source paper "correcting for selection bias in learning to rank systems"):
  with members:
    - "to model or to intervene a comparison of counterfactual and online learning to rank from user interactions"
    - "differentiable unbiased online learning to rank"
    - "unbiased learning to rank with unbiased propensity estimation"
    - "unbiased lambdamart an unbiased pairwise learning to rank algorithm"
    - "the deconfounded recommender a causal inference approach to recommendation"
    - "how algorithmic confounding in recommendation systems increases homogeneity and decreases utility"
    - "a general framework for counterfactual learning to rank"
    - "unbiased learning to rank with biased feedback"

Connected through hyperedge HE-3 (induced by source paper "fighting fire with fire using antidote data to improve polarization and fairness of recommender systems"):
  with members:
    - "equity of attention amortizing individual fairness in rankings"
    - "how algorithmic confounding in recommendation systems increases homogeneity and decreases utility"
    - "fairness of exposure in rankings"
    - "a reductions approach to fair classification"
    - "stealing hyperparameters in machine learning"
    - "data decisions and theoretical implications when adversarially learning fair representations"
    - "a convex framework for fair regression"
    - "beyond parity fairness objectives for collaborative filtering"

Connected through hyperedge HE-4 (induced by source paper "embedding based qualitative analysis of polarization in turkey"):
  with members:
    - "unsupervised user stance detection on twitter"
    - "devam vs tamam 2018 turkish elections"
    - "community interaction and conflict on the web"
    - "to kavanaugh or not to kavanaugh that is the polarizing question"
    - "word embeddings quantify 100 years of gender and ethnic stereotypes"
    - "semantics derived automatically from language corpora contain human like biases"
    - "enriching word vectors with subword information"
    - "bag of tricks for efficient text classification"

Connected through hyperedge HE-5 (induced by source paper "the few get richer a surprising consequence of popularity based rankings"):
  with members:
    - "equity of attention amortizing individual fairness in rankings"
    - "how algorithmic popularity bias hinders or promotes quality"
    - "leveraging the crowd to detect and reduce the spread of fake news and misinformation"
    - "opinion dynamics via search engines and other algorithmic gatekeepers"
    - "anyone can become a troll causes of trolling behavior in online discussions"
    - "fa ir a fair top k ranking algorithm"
    - "topical interests and the mitigation of search engine bias"

Connected through hyperedge HE-6 (induced by source paper "understanding filter bubbles and polarization in social networks"):
  with members:
    - "opinion dynamics with varying susceptibility to persuasion"
    - "eigenvector computation and community detection in asynchronous gossip models"
    - "how algorithmic confounding in recommendation systems increases homogeneity and decreases utility"
    - "maximizing the diversity of exposure in a social network"
    - "find your place simple distributed algorithms for community detection"
    - "opinion maximization in social networks"
    - "how bad is forming your own opinion"

Connected through hyperedge HE-7 (induced by source paper "computational methods in professional communication"):
  with members:
    - "what is gab a bastion of free speech or an alt right echo chamber"
    - "a nutritional label for rankings"
    - "intertemporal connections between query suggestions and search engine results for politics related queries"
    - "constructing bibliometric networks a comparison between full and fractional counting"
    - "four degrees of separation"
    - "indicators of the interdisciplinarity of journals diversity centrality and citations"

Connected through hyperedge HE-8 (induced by source paper "a geometric model of opinion polarization"):
  with members:
    - "on the inevitability of online echo chambers"
    - "balancing information exposure in social networks"
    - "novel multidimensional models of opinion dynamics in social networks"
    - "reducing controversy by connecting opposing views"
    - "the evolution of beliefs over signed social networks"

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)