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 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.

Connected through hyperedge HE-1 (induced by source paper "the complexity of contracts"):
  with members:
    - "delegated search approximates efficient search"
    - "a duality based unified approach to bayesian mechanism design"
    - "simple versus optimal contracts"
    - "bernoulli factories and black box reductions in mechanism design"
    - "the menu size complexity of revenue approximation"
    - "mixture selection mechanism design and signaling"
    - "non optimal mechanism design"
    - "a simple and approximately optimal mechanism for an additive buyer"

Connected through hyperedge HE-2 (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"
    - "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"

Connected through hyperedge HE-3 (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"
    - "persuasion with limited communication"
    - "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-4 (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"
    - "algorithmic persuasion with no externalities"
    - "the competition complexity of auctions a bulow klemperer result for multi dimensional bidders"
    - "computational aspects of private bayesian persuasion"

Connected through hyperedge HE-5 (induced by source paper "on the tractability of public persuasion with no externalities"):
  with members:
    - "algorithmic persuasion with no externalities"
    - "bayesian exploration incentivizing exploration in bayesian games"
    - "no regret learning in bayesian games"
    - "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-6 (induced by source paper "algorithmic price discrimination"):
  with members:
    - "settling the sample complexity of single parameter revenue maximization"
    - "learning optimal reserve price against non myopic bidders"
    - "learning multi item auctions with or without samples"
    - "learning simple auctions"
    - "bayesian exploration incentivizing exploration in bayesian games"
    - "efficient empirical revenue maximization in single parameter auction environments"
    - "sample complexity for non truthful mechanisms"
    - "the sample complexity of revenue maximization"

Connected through hyperedge HE-7 (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"
    - "algorithmic persuasion with no externalities"
    - "computational aspects of private 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-8 (induced by source paper "a game theoretic approach to adversarial linear support vector classification"):
  with members:
    - "adequacy of the gradient descent method for classifier evasion attacks"
    - "adversarial examples attacks and defenses for deep learning"
    - "adversarial machine learning at scale"
    - "distillation as a defense to adversarial perturbations against deep neural networks"
    - "estimation with strategic sensors"
    - "evasion attacks against machine learning at test time"

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)