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: "send mixed signals earn more work less"
  Abstract: Emek et al. presented a model of probabilistic single-item second price auctions where an auctioneer who is informed about the type of an item for sale, broadcasts a signal about this type to uninformed bidders. They proved that finding the optimal (for the purpose of generating revenue) {\em pure} signaling scheme is strongly NP-hard. In contrast, we prove that finding the optimal {\em mixed} signaling scheme can be done in polynomial time using linear programming. For the proof, we show that the problem is strongly related to a problem of optimally bundling divisible goods for auctioning. We also prove that a mixed signaling scheme can in some cases generate twice as much revenue as the best pure signaling scheme and we prove a generally applicable lower bound on the revenue generated by the best mixed signaling scheme.

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"
    - "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-2 (induced by source paper "simple mechanisms for profit maximization in multi item auctions"):
  with members:
    - "the best of both worlds asymptotically efficient mechanisms with a guarantee on the expected gains from trade"
    - "a duality based unified approach to bayesian mechanism design"
    - "approximating gains from trade in two sided markets via simple mechanisms"
    - "approximate revenue maximization with multiple items"
    - "simple mechanisms for subadditive buyers via duality"
    - "almost efficient mechanisms for bilateral trading"
    - "mechanism design for subadditive agents via an ex ante relaxation"
    - "approximately efficient two sided combinatorial auctions"

Connected through hyperedge HE-3 (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"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "selling information through consulting"):
  with members:
    - "targeting and signaling in ad auctions"
    - "algorithmic bayesian persuasion"
    - "optimal mechanisms for selling information"

Connected through hyperedge HE-5 (induced by source paper "simple versus optimal contracts"):
  with members:
    - "how do classifiers induce agents to invest effort strategically"
    - "delegated search approximates efficient search"
    - "adaptive contract design for crowdsourcing markets bandit algorithms for repeated principal agent problems"
    - "constrained signaling in auction design"
    - "mixed strategies in combinatorial agency"

Connected through hyperedge HE-6 (induced by source paper "targeting and signaling in ad auctions"):
  with members:
    - "algorithmic persuasion with no externalities"
    - "computational aspects of private bayesian persuasion"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "algorithmic persuasion with no externalities"):
  with members:
    - "computational aspects of private bayesian persuasion"
    - "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"
    - "optimal multi dimensional mechanism design reducing revenue to welfare maximization"

Connected through hyperedge HE-8 (induced by source paper "persuasion with limited communication"):
  with members:
    - "bayesian incentive compatible bandit exploration"
    - "algorithmic bayesian persuasion"
    - "mixture selection mechanism design and signaling"
    - "on the hardness of signaling"
    - "constrained signaling in auction design"
    - "revenue maximization via hiding item attributes"
    - "auctions with severely bounded communication"

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)