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 "public bayesian persuasion being almost optimal and almost persuasive"):
  Abstract: Persuasion studies how an informed principal may influence the behavior of agents by the strategic provision of payoff-relevant information. We focus on the fundamental multi-receiver model by Arieli and Babichenko (2019), in which there are no inter-agent externalities. Unlike prior works on this problem, we study the public persuasion problem in the general setting with: (i) arbitrary state spaces; (ii) arbitrary action spaces; (iii) arbitrary sender's utility functions. We fully characterize the computational complexity of computing a bi-criteria approximation of an optimal public signaling scheme. In particular, we show, in a voting setting of independent interest, that solving this problem requires at least a quasi-polynomial number of steps even in settings with a binary action space, assuming the Exponential Time Hypothesis. In doing so, we prove that a relaxed version of the Maximum Feasible Subsystem of Linear Inequalities problem requires at least quasi-polynomial time to be solved. Finally, we close the gap by providing a quasi-polynomial time bi-criteria approximation algorithm for arbitrary public persuasion problems that, in specific settings, yields a QPTAS.

Includes members:
  Member "persuading voters it s easy to whisper it s hard to speak loud":
    also connected through other hyperedges:
      (none)
  Member "on the tractability of public persuasion with no externalities":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "signaling in bayesian network congestion games the subtle power of symmetry")
  Member "targeting and signaling in ad auctions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "bayesian persuasion with sequential games")
  Member "algorithmic persuasion with no externalities":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "persuasion and incentives through the lens of duality")
  Member "computational aspects of private bayesian persuasion":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "persuasion and incentives through the lens of duality")
  Member "bayesian exploration incentivizing exploration in bayesian games":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "incentivising exploration and recommendations for contextual bandits with payments")
  Member "inapproximability results for approximate nash equilibria":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "computing constrained approximate equilibria in polymatrix games")
  Member "can almost everybody be almost happy pcp for ppad and the inapproximability of nash":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "well supported versus approximate nash equilibria query complexity of large games")

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)