human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "understanding filter bubbles and polarization in social networks"):
  Abstract: Recent studies suggest that social media usage -- while linked to an increased diversity of information and perspectives for users -- has exacerbated user polarization on many issues. A popular theory for this phenomenon centers on the concept of "filter bubbles": by automatically recommending content that a user is likely to agree with, social network algorithms create echo chambers of similarly-minded users that would not have arisen otherwise. However, while echo chambers have been observed in real-world networks, the evidence for filter bubbles is largely post-hoc. #R##N#In this work, we develop a mathematical framework to study the filter bubble theory. We modify the classic Friedkin-Johnsen opinion dynamics model by introducing another actor, the network administrator, who filters content for users by making small changes to the edge weights of a social network (for example, adjusting a news feed algorithm to change the level of interaction between users). #R##N#On real-world networks from Reddit and Twitter, we show that when the network administrator is incentivized to reduce disagreement among users, even relatively small edge changes can result in the formation of echo chambers in the network and increase user polarization. We theoretically support this observed sensitivity of social networks to outside intervention by analyzing synthetic graphs generated from the stochastic block model. Finally, we show that a slight modification to the incentives of the network administrator can mitigate the filter bubble effect while minimally affecting the administrator's target objective, user disagreement.

Includes members:
  Member "opinion dynamics with varying susceptibility to persuasion":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "network disruption maximizing disagreement and polarization in social networks")
  Member "eigenvector computation and community detection in asynchronous gossip models":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "step by step community detection for volume regular graphs")
  Member "how algorithmic confounding in recommendation systems increases homogeneity and decreases utility":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "fairness in learning based sequential decision algorithms a survey")
  Member "maximizing the diversity of exposure in a social network":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "diversifying seeds and audience in social influence maximization")
  Member "find your place simple distributed algorithms for community detection":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "efficient distributed community detection in the stochastic block model")
  Member "opinion maximization in social networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "angry birds flock together aggression propagation on social media")
  Member "biased assimilation homophily and the dynamics of polarization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "vocabulary based method for quantifying controversy in social media")
  Member "how bad is forming your own opinion":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "adversarial perturbations of opinion dynamics in networks")

Question: Please tell me which class the source paper 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)