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: "algorithmic persuasion with no externalities"
  Abstract: We study the algorithmics of information structure design -- a.k.a. persuasion or signaling -- in a fundamental special case introduced by Arieli and Babichenko: multiple agents, binary actions, and no inter-agent externalities. Unlike prior work on this model, we allow many states of nature. We assume that the principal's objective is a monotone set function, and study the problem both in the public signal and private signal models, drawing a sharp contrast between the two in terms of both efficacy and computational complexity. #R##N#When private signals are allowed, our results are largely positive and quite general. First, we show polynomial-time equivalence between optimal signaling and the problem of maximizing the objective function plus an additive function. This yields an efficient implementation of the optimal scheme when the objective is supermodular or anonymous. Second, we exhibit a (1-1/e)-approximation of the optimal private signaling scheme, modulo an additive loss of $\epsilon$, when the objective function is submodular. These two results simplify, unify, and generalize results of [Arieli and Babichenko, 2016] and [Babichenko and Barman, 2016], extending them from a binary state of nature to many states (modulo the additive loss in the latter result). Third, we consider the binary-state case with a submodular objective, and simplify and slightly strengthen the result of [Babichenko and Barman, 2016] to obtain a (1-1/e)-approximation via an explicitly constructed scheme. #R##N#When only a public signal is allowed, our results are negative. First, we show that it is NP-hard to approximate the optimal public scheme, within any constant factor, even when the objective is additive. Second, we show that the optimal private scheme can outperform the optimal public scheme, in terms of maximizing the sender's objective, by a polynomial factor.

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"
    - "on the tractability of public persuasion with no externalities"
    - "targeting and signaling in ad auctions"
    - "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 "persuasion and incentives through the lens of duality"):
  with members:
    - "a duality based unified approach to bayesian mechanism design"
    - "approximating gains from trade in two sided markets via simple mechanisms"
    - "on the competition complexity of dynamic mechanism design"
    - "the value of information concealment"
    - "simple mechanisms for subadditive buyers via duality"
    - "the competition complexity of auctions a bulow klemperer result for multi dimensional bidders"
    - "computational aspects of private bayesian persuasion"
    - "algorithmic bayesian persuasion"

Connected through hyperedge HE-3 (induced by source paper "on the tractability of public persuasion with no externalities"):
  with members:
    - "bayesian exploration incentivizing exploration in bayesian games"
    - "no regret learning in bayesian games"
    - "algorithmic bayesian persuasion"
    - "deterministic algorithms for submodular maximization problems"
    - "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"
    - "mixture selection mechanism design and signaling"
    - "tight bounds on ell_1 approximation and learning of self bounding functions"

Connected through hyperedge HE-4 (induced by source paper "persuading voters it s easy to whisper it s hard to speak loud"):
  with members:
    - "on the tractability of public persuasion with no externalities"
    - "defending elections against malicious spread of misinformation"
    - "controlling elections through social influence"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "the secretary recommendation problem"):
  with members:
    - "delegated search approximates efficient search"
    - "combinatorial secretary problems with ordinal information"
    - "beyond matroids secretary problem and prophet inequality with general constraints"
    - "algorithmic bayesian persuasion"
    - "online independent set beyond the worst case secretaries prophets and periods"

Connected through hyperedge HE-6 (induced by source paper "optimal algorithm for bayesian incentive compatible exploration"):
  with members:
    - "competing bandits learning under competition"
    - "bayesian exploration incentivizing exploration in bayesian games"
    - "persuasion with limited communication"
    - "bayesian incentive compatible bandit exploration"
    - "algorithmic bayesian persuasion"

Connected through hyperedge HE-7 (induced by source paper "information design in spatial resource competition"):
  with members:
    - "information signal design for incentivizing team formation"
    - "targeting and signaling in ad auctions"
    - "computational aspects of private bayesian persuasion"

Connected through hyperedge HE-8 (induced by source paper "access to population level signaling as a source of inequality"):
  with members:
    - "preventing fairness gerrymandering auditing and learning for subgroup fairness"
    - "fairness incentives for myopic agents"
    - "fairness at equilibrium in the labor market"
    - "fairness in learning classic and contextual bandits"
    - "equality of opportunity in supervised learning"
    - "on the im possibility of fairness"
    - "inherent trade offs in the fair determination of risk scores"
    - "algorithmic bayesian persuasion"

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)