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 "persuading voters it s easy to whisper it s hard to speak loud"):
  Abstract: We focus on the following natural question: is it possible to influence the outcome of a voting process through the strategic provision of information to voters who update their beliefs rationally? We investigate whether it is computationally tractable to design a signaling scheme maximizing the probability with which the sender's preferred candidate is elected. We focus on the model recently introduced by Arieli and Babichenko (2019) (i.e., without inter-agent externalities), and consider, as explanatory examples, $k$-voting rule and plurality voting. There is a sharp contrast between the case in which private signals are allowed and the more restrictive setting in which only public signals are allowed. In the former, we show that an optimal signaling scheme can be computed efficiently both under a $k$-voting rule and plurality voting. In establishing these results, we provide two general (i.e., applicable to settings beyond voting) contributions. Specifically, we extend a well known result by Dughmi and Xu (2017) to more general settings, and prove that, when the sender's utility function is anonymous, computing an optimal signaling scheme is fixed parameter tractable w.r.t. the number of receivers' actions. In the public signaling case, we show that the sender's optimal expected return cannot be approximated to within any factor under a $k$-voting rule. This negative result easily extends to plurality voting and problems where utility functions are anonymous.

Includes members:
  Member "on the tractability of public persuasion with no externalities":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "public bayesian persuasion being almost optimal and almost persuasive")
  Member "defending elections against malicious spread of misinformation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "exploiting social influence to control elections based on scoring rules")
  Member "controlling elections through social influence":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "election manipulation on social networks seeding edge removal edge addition")
  Member "algorithmic persuasion with no externalities":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "public bayesian persuasion being almost optimal and almost persuasive")
  Member "computational aspects of private bayesian persuasion":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "public bayesian persuasion being almost optimal and almost persuasive")
  Member "algorithmic bayesian persuasion":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "the complexity of contracts")
  Member "hardness results for signaling in bayesian zero sum and network routing games":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "public bayesian persuasion being almost optimal and almost persuasive")
  Member "honest signaling in zero sum games is hard and lying is even harder":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "public bayesian persuasion being almost optimal and almost persuasive")

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.GT(Computer Science and Game Theory)