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: "computational aspects of private bayesian persuasion"
  Abstract: We study computational questions in a game-theoretic model that, in particular, aims to capture advertising/persuasion applications such as viral marketing. Specifically, we consider a multi-agent Bayesian persuasion model where an informed sender (marketer) tries to persuade a group of agents (consumers) to adopt a certain product. The quality of the product is known to the sender, but it is unknown to the agents. The sender is allowed to commit to a signaling policy where she sends a private signal---say, a viral marketing ad---to every agent. This work studies the computation aspects of finding a signaling policy that maximizes the sender's revenue. #R##N#We show that if the sender's utility is a submodular function of the set of agents that adopt the product, then we can efficiently find a signaling policy whose revenue is at least (1-1/e) times the optimal. We also prove that approximating the sender's optimal revenue by a factor better than (1-1/e) is NP-hard and, hence, the developed approximation guarantee is essentially tight. When the senders' utility is a function of the number of agents that adopt the product (i.e., the utility function is anonymous), we show that an optimal signaling policy can be computed in polynomial time. Our results are based on an interesting connection between the Bayesian persuasion problem and the evaluation of the concave closure of a set 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"
    - "on the tractability of public persuasion with no externalities"
    - "targeting and signaling in ad auctions"
    - "algorithmic persuasion with no externalities"
    - "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"
    - "algorithmic persuasion with no externalities"
    - "the competition complexity of auctions a bulow klemperer result for multi dimensional bidders"
    - "algorithmic bayesian persuasion"

Connected through hyperedge HE-3 (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"
    - "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-4 (induced by source paper "bayesian persuasion with sequential games"):
  with members:
    - "targeting and signaling in ad auctions"
    - "no regret learning in bayesian games"
    - "algorithmic bayesian persuasion"
    - "constrained signaling in auction design"
    - "sequential auctions and externalities"

Connected through hyperedge HE-5 (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"
    - "algorithmic persuasion with no externalities"

Connected through hyperedge HE-6 (induced by source paper "targeting and signaling in ad auctions"):
  with members:
    - "algorithmic persuasion with no externalities"
    - "the menu size complexity of revenue approximation"
    - "algorithmic bayesian persuasion"
    - "mixture selection mechanism design and signaling"
    - "constrained signaling in auction design"
    - "the menu size complexity of auctions"
    - "send mixed signals earn more work less"

Connected through hyperedge HE-7 (induced by source paper "algorithmic persuasion with no externalities"):
  with members:
    - "algorithmic bayesian persuasion"
    - "hardness results for signaling in bayesian zero sum and network routing games"
    - "mixture selection mechanism design and signaling"
    - "on the hardness of signaling"
    - "understanding incentives mechanism design becomes algorithm design"
    - "constrained signaling in auction design"
    - "send mixed signals earn more work less"
    - "optimal multi dimensional mechanism design reducing revenue to welfare maximization"

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)