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: "minimizing polarization and disagreement in social networks"
  Abstract: The rise of social media and online social networks has been a disruptive force in society. Opinions are increasingly shaped by interactions on online social media, and social phenomena including disagreement and polarization are now tightly woven into everyday life. In this work we initiate the study of the following question: given $n$ agents, each with its own initial opinion that reflects its core value on a topic, and an opinion dynamics model, what is the structure of a social network that minimizes {\em polarization} and {\em disagreement} simultaneously? #R##N#This question is central to recommender systems: should a recommender system prefer a link suggestion between two online users with similar mindsets in order to keep disagreement low, or between two users with different opinions in order to expose each to the other's viewpoint of the world, and decrease overall levels of polarization? Our contributions include a mathematical formalization of this question as an optimization problem and an exact, time-efficient algorithm. We also prove that there always exists a network with $O(n/\epsilon^2)$ edges that is a $(1+\epsilon)$ approximation to the optimum. For a fixed graph, we additionally show how to optimize our objective function over the agents' innate opinions in polynomial time. #R##N#We perform an empirical study of our proposed methods on synthetic and real-world data that verify their value as mining tools to better understand the trade-off between of disagreement and polarization. We find that there is a lot of space to reduce both polarization and disagreement in real-world networks; for instance, on a Reddit network where users exchange comments on politics, our methods achieve a $\sim 60\,000$-fold reduction in polarization and disagreement.

Connected through hyperedge HE-1 (induced by source paper "choice set optimization under discrete choice models of group decisions"):
  with members:
    - "learning choice functions"
    - "learning mixtures of plackett luce models from structured partial orders"
    - "discovering context effects from raw choice data"
    - "on the convergence of adam and beyond"
    - "learning choice functions concepts and architectures"
    - "pytorch an imperative style high performance deep learning library"
    - "predicting choice with set dependent aggregation"
    - "reasoning in bayesian opinion exchange networks is pspace hard"

Connected through hyperedge HE-2 (induced by source paper "adversarial perturbations of opinion dynamics in networks"):
  with members:
    - "opinion maximization in social networks"
    - "how bad is forming your own opinion"
    - "twice ramanujan sparsifiers"
    - "spectral sparsification of graphs"
    - "max cut and the smallest eigenvalue"

Connected through hyperedge HE-3 (induced by source paper "network disruption maximizing disagreement and polarization in social networks"):
  with members:
    - "adversarial perturbations of opinion dynamics in networks"
    - "analysis of strategy and spread of russia sponsored content in the us in 2017"
    - "opinion dynamics with varying susceptibility to persuasion"
    - "some like it hoax automated fake news detection in social networks"
    - "opinion maximization in social networks"

Connected through hyperedge HE-4 (induced by source paper "shifting opinions in a social network through leader selection"):
  with members:
    - "current flow group closeness centrality for complex networks"
    - "opinion dynamics with varying susceptibility to persuasion"
    - "opinion dynamics with stubborn agents"
    - "from local averaging to emergent global behaviors the fundamental role of network interconnections"
    - "opinion maximization in social networks"
    - "an efficient parallel solver for sdd linear systems"
    - "submodular optimization with submodular cover and submodular knapsack constraints"
    - "minimizing convergence error in multi agent systems via leader selection a supermodular optimization approach"

Connected through hyperedge HE-5 (induced by source paper "reconciliation k median clustering with non polarized representatives"):
  with members:
    - "constant factor approximation for ordered k median"
    - "balancing information exposure in social networks"
    - "joint non negative matrix factorization for learning ideological leaning on twitter"
    - "reducing controversy by connecting opposing views"
    - "better guarantees for k means and euclidean k median by primal dual algorithms"
    - "an improved approximation for k median and positive correlation in budgeted optimization"
    - "bi factor approximation algorithms for hard capacitated k median problems"
    - "approximating k median via pseudo approximation"

Connected through hyperedge HE-6 (induced by source paper "gaps in information access in social networks"):
  with members:
    - "racial categories in machine learning"
    - "fairness behind a veil of ignorance a welfare analysis for automated decision making"
    - "equality of opportunity in supervised learning"
    - "a confidence based approach for balancing fairness and accuracy"
    - "reducing controversy by connecting opposing views"
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "fairness through awareness"
    - "pricing strategies for viral marketing on social networks"

Connected through hyperedge HE-7 (induced by source paper "disagreement and polarization in two party social networks"):
  with members:
    - "shifting opinions in a social network through leader selection"
    - "opinion dynamics with varying susceptibility to persuasion"
    - "opinion dynamics with stubborn agents"
    - "from local averaging to emergent global behaviors the fundamental role of network interconnections"
    - "opinion maximization in social networks"
    - "opinion fluctuations and disagreement in social networks"
    - "twice ramanujan sparsifiers"

Connected through hyperedge HE-8 (induced by source paper "maximizing contrasting opinions in signed social networks"):
  with members:
    - "the wisdom of polarized crowds"
    - "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"

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)