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 "algorithmic bayesian persuasion"):
  Abstract: Persuasion, defined as the act of exploiting an informational advantage in order to effect the decisions of others, is ubiquitous. Indeed, persuasive communication has been estimated to account for almost a third of all economic activity in the US. This paper examines persuasion through a computational lens, focusing on what is perhaps the most basic and fundamental model in this space: the celebrated Bayesian persuasion model of Kamenica and Gentzkow. Here there are two players, a sender and a receiver. The receiver must take one of a number of actions with a-priori unknown payoff, and the sender has access to additional information regarding the payoffs. The sender can commit to revealing a noisy signal regarding the realization of the payoffs of various actions, and would like to do so as to maximize her own payoff assuming a perfectly rational receiver. #R##N#We examine the sender's optimization task in three of the most natural input models for this problem, and essentially pin down its computational complexity in each. When the payoff distributions of the different actions are i.i.d. and given explicitly, we exhibit a polynomial-time (exact) algorithm, and a "simple" $(1-1/e)$-approximation algorithm. Our optimal scheme for the i.i.d. setting involves an analogy to auction theory, and makes use of Border's characterization of the space of reduced-forms for single-item auctions. When action payoffs are independent but non-identical with marginal distributions given explicitly, we show that it is #P-hard to compute the optimal expected sender utility. Finally, we consider a general (possibly correlated) joint distribution of action payoffs presented by a black box sampling oracle, and exhibit a fully polynomial-time approximation scheme (FPTAS) with a bi-criteria guarantee. We show that this result is the best possible in the black-box model for information-theoretic reasons.

Includes members:
  Member "bayesian incentive compatible bandit exploration":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "incentivizing exploration with selective data disclosure")
  Member "public projects boolean functions and the borders of border s theorem":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "complexity theory game theory and economics the barbados lectures")
  Member "mixture selection mechanism design and signaling":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "the complexity of contracts")
  Member "on the hardness of signaling":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the complexity of contracts")
  Member "constrained signaling in auction design":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "the complexity of contracts")
  Member "revenue maximization via hiding item attributes":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "access to population level signaling as a source of inequality")
  Member "bayesian optimal auctions via multi to single agent reduction":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a permutation equivariant neural network architecture for auction design")
  Member "send mixed signals earn more work less":
    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)