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 "election control with voters uncertainty hardness and approximation results"):
  Abstract: The election control problem asks to find a set of nodes in a social network of voters to be the starters of a political campaign aimed at supporting a given target candidate. When a voter is reached by the campaign it changes its opinion on the candidates. The goal is to shape the diffusion of the campaign in such a way that the chances of victory of the target candidate are maximized. #R##N#Previous works on this problem show that it can be approximated within a constant ratio in several models for information diffusion and voting systems. These works assume the full knowledge of the preferences of each voter. However, this information is not always available since voters can be undecided or they may not want to reveal it. Herein we relax this assumption by considering that each voter is associated with a probability distribution over the candidates. #R##N#We propose two new models in which, when a voter is reached by a campaign, it modifies its probability distribution according to the amount of influence it received from its neighbors in the network. We then study the election control problem on the new models: In the first model, under the Gap-ETH hypothesis, election control cannot be approximated within a factor better than $1/n^{o(1)}$, where $n$ is the number of voters; in the second model the problem admits a constant factor approximation algorithm.

Includes members:
  Member "vote for me election control via social influence in arbitrary scoring rule voting systems":
    also connected through other hyperedges:
      (none)
  Member "election campaigning on social media politicians audiences and the mediation of political communication on facebook and twitter":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "political elections under social fire analysis and detection of propaganda on twitter")
  Member "election with bribed voter uncertainty hardness and approximation algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "person job fit adapting the right talent for the right job with joint representation learning")
  Member "disinformation and social bot operations in the run up to the 2017 french presidential election":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "don t feed the troll detecting troll behavior via inverse reinforcement learning")
  Member "controlling elections through social influence":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "election manipulation on social networks seeding edge removal edge addition")
  Member "almost polynomial ratio eth hardness of approximating densest k subgraph":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "optimizing item and subgroup configurations for social aware vr shopping")
  Member "complexity of shift bribery in committee elections":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on the complexity of destructive bribery in approval based multi winner voting")
  Member "minority becomes majority in social networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "election manipulation on social networks seeding edge removal edge addition")

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)