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: "persuasion with limited communication"
  Abstract: We examine information structure design, also called "persuasion" or "signaling," in the presence of a constraint on the amount of communication. We focus on the fundamental setting of bilateral trade, which in its simplest form involves a seller with a single item to price, a buyer whose value for the item is drawn from a common prior distribution over n different possible values, and a take-it-or-leave-it-offer protocol. A mediator with access to the buyer's type may partially reveal such information to the seller in order to further some objective such as the social welfare or the seller's revenue. We study how a limit on the number of bits of communication affects this setting in two respects: (1) How much does this constraint reduce the optimal welfare or revenue? (2) What effect does constraining communication have on the computational complexity of the mediator's optimization problem?   In the setting of maximizing welfare under bilateral trade, we exhibit positive answers for both questions (1) and (2). Whereas the optimal unconstrained scheme may involve n signals (and thus log(n) bits of communication), we show that O(log(n) log 1/e) signals suffice for a 1--e approximation to the optimal welfare, and this bound is tight. This largely justifies the design of algorithms for signaling subject to drastic limits on communication. As our main result, we exhibit an efficient algorithm for computing a M-1/M ⋅ (1--1/e)-approximation to the welfare-maximizing scheme with at most M signals. This result hinges on an intricate submodularity argument which relies on the optimality of a greedy algorithm for solving a certain linear program. For the revenue objective, the surprising logarithmic bound on the number of signals does not carry over: we show that Ω(n) signals are needed for a constant factor approximation to the revenue of a fully informed seller. From a computational perspective, however, the problem gets easier: we show that a simple dynamic program computes the signaling scheme with M signals maximizing the seller's revenue.   Observing that the signaling problem in bilateral trade is a special case of the fundamental Bayesian Persuasion model of Kamenica and Gentzkow, we also examine the question of communication-constrained signaling more generally. Specifically, in this model there is a sender (the mediator), a receiver (the seller) looking to take an action (setting the price), and a state of nature (the buyer's type) drawn from a common prior. The state of nature encodes both the receiver's utility and the sender's objective as a function of the receiver's action. Our results for bilateral trade with the revenue objective imply that limiting communication to M signals can scale the sender's utility by a factor of O(M/n) in general, where $n$ denotes the number of states of nature. We also show that our positive algorithmic results for bilateral trade do not extend to communication-constrained signaling in the Bayesian Persuasion model. Specifically, we show that it is NP-hard to approximate the optimal sender's utility to within any constant factor in the presence of communication constraints.

Connected through hyperedge HE-1 (induced by source paper "strategic communication with side information at the decoder"):
  with members:
    - "strong coordination of signals and actions over noisy channels with two sided state information"
    - "persuasion with limited communication capacity"
    - "information design for strategic coordination of autonomous devices with non aligned utilities"
    - "information theoretic approach to strategic communication as a hierarchical game"
    - "on the role of side information in strategic communication"
    - "algorithmic bayesian persuasion"
    - "empirical coordination with two sided state information and correlated source and state"
    - "the henchman problem measuring secrecy by the minimum distortion in a list"

Connected through hyperedge HE-2 (induced by source paper "communication distortion and randomness in metric voting"):
  with members:
    - "comparing election methods where each voter ranks only few candidates"
    - "improved metric distortion for deterministic social choice rules"
    - "approval based elections and distortion of voting rules"
    - "an analysis framework for metric voting based on lp duality"
    - "random dictators with a random referee constant sample complexity mechanisms for social choice"
    - "of the people voting is more effective with representative candidates"
    - "on the distortion of voting with multiple representative candidates"
    - "combinatorial auctions do need modest interaction"

Connected through hyperedge HE-3 (induced by source paper "optimal algorithm for bayesian incentive compatible exploration"):
  with members:
    - "competing bandits learning under competition"
    - "algorithmic persuasion with no externalities"
    - "bayesian exploration incentivizing exploration in bayesian games"
    - "bayesian incentive compatible bandit exploration"
    - "algorithmic bayesian persuasion"

Connected through hyperedge HE-4 (induced by source paper "robust commitments and partial reputation"):
  with members:
    - "algorithmic bayesian persuasion"
    - "computational rationalization the inverse equilibrium problem"
    - "constrained signaling in auction design"
    - "stackelberg vs nash in security games an extended investigation of interchangeability equivalence and uniqueness"

Connected through hyperedge HE-5 (induced by source paper "imitative follower deception in stackelberg games"):
  with members:
    - "watch and learn optimizing from revealed preferences feedback"

Connected through hyperedge HE-6 (induced by source paper "information theoretic limits of strategic communication"):
  with members:
    - "information design for strategic coordination of autonomous devices with non aligned utilities"
    - "information theoretic approach to strategic communication as a hierarchical game"
    - "algorithmic bayesian persuasion"
    - "constrained source coding with side information"
    - "estimation with strategic sensors"
    - "nash codes for noisy channels"
    - "perfect output feedback in the two user decentralized interference channel"
    - "rate distortion theory for secrecy systems"

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)