human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "on the tractability of public persuasion with no externalities"
  Abstract: Persuasion studies how a principal can influence agents' decisions via strategic information revelation --- often described as a signaling scheme --- in order to yield the most desirable equilibrium outcome. Recently, there has been a large body of algorithmic study of designing optimal public signaling schemes, a.k.a., public persuasion, which however is rifle with computational intractability results. In this paper, we design efficient and tight algorithms for public persuasion, and focus on a fundamental multi-agent persuasion model with no inter-agent externalities and binary actions introduced by Arieli and Babichenko. En route, we develop new algorithmic techniques which may be of independent interests. #R##N#First, we prove that optimal public persuasion is fixed parameter tractable. Our main result here relies on an interesting connection to a basic question in combinatorial geometry: how many cells can $n$ hyperplanes divide $\RR^d$ into? We use this connection to show a new characterization of public persuasion, which then enables efficient algorithm design. Second, we relax agent incentives and show that optimal public persuasion admits a bi-criteria PTAS for monotone submodular objectives and this approximation is tight. To prove this result, we establish an intriguing ``noise stability'' property of submodular functions which strictly generalizes the key result of Cheraghchi et al., originally motivated by applications of learning submodular functions and differential privacy. Finally, motivated by automated persuasion implemented as software, we consider relaxing the equilibrium concept of the model to coarse correlated equilibrium. Here we use a sophisticated primal-dual analysis to establish the polynomial-time equivalence between optimal public persuasion and the combinatorial problem of directly maximizing the sender's objective minus any linear function.

Connected through hyperedge HE-1 (induced by source paper "public bayesian persuasion being almost optimal and almost persuasive"):
  with members:
    - "persuading voters it s easy to whisper it s hard to speak loud"
    - "targeting and signaling in ad auctions"
    - "algorithmic persuasion with no externalities"
    - "computational aspects of private bayesian persuasion"
    - "bayesian exploration incentivizing exploration in bayesian games"
    - "inapproximability results for approximate nash equilibria"
    - "can almost everybody be almost happy pcp for ppad and the inapproximability of nash"
    - "algorithmic bayesian persuasion"

Connected through hyperedge HE-2 (induced by source paper "signaling in bayesian network congestion games the subtle power of symmetry"):
  with members:
    - "bayesian persuasion with sequential games"
    - "value of information systems in routing games"
    - "informational braess paradox the effect of information on traffic congestion"
    - "finding any nontrivial coarse correlated equilibrium is hard"
    - "a general framework for computing optimal correlated equilibria in compact games"

Connected through hyperedge HE-3 (induced by source paper "persuading voters it s easy to whisper it s hard to speak loud"):
  with members:
    - "defending elections against malicious spread of misinformation"
    - "controlling elections through social influence"
    - "algorithmic persuasion with no externalities"
    - "computational aspects of private bayesian persuasion"
    - "algorithmic bayesian persuasion"
    - "hardness results for signaling in bayesian zero sum and network routing games"
    - "honest signaling in zero sum games is hard and lying is even harder"
    - "elections with few voters candidate control can be easy"

Question: Please tell me which class the center node 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)