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: "pricing ad slots with consecutive multi unit demand"
  Abstract: We consider the optimal pricing problem for a model of the rich media advertisement market, as well as other related applications. In this market, there are multiple buyers (advertisers), and items (slots) that are arranged in a line such as a banner on a website. Each buyer desires a particular number of {\em consecutive} slots and has a per-unit-quality value $v_i$ (dependent on the ad only) while each slot $j$ has a quality $q_j$ (dependent on the position only such as click-through rate in position auctions). Hence, the valuation of the buyer $i$ for item $j$ is $v_iq_j$. We want to decide the allocations and the prices in order to maximize the total revenue of the market maker. #R##N#A key difference from the traditional position auction is the advertiser's requirement of a fixed number of consecutive slots. Consecutive slots may be needed for a large size rich media ad. We study three major pricing mechanisms, the Bayesian pricing model, the maximum revenue market equilibrium model and an envy-free solution model. Under the Bayesian model, we design a polynomial time computable truthful mechanism which is optimum in revenue. For the market equilibrium paradigm, we find a polynomial time algorithm to obtain the maximum revenue market equilibrium solution. In envy-free settings, an optimal solution is presented when the buyers have the same demand for the number of consecutive slots. We conduct a simulation that compares the revenues from the above schemes and gives convincing results.

Connected through hyperedge HE-1 (induced by source paper "pricing to maximize revenue and welfare simultaneously in large markets"):
  with members:
    - "do prices coordinate markets"
    - "the sample complexity of revenue maximization"
    - "envy free pricing in large markets approximating revenue and welfare"
    - "applications of alpha strongly regular distributions to bayesian auctions"
    - "optimising trade offs among stakeholders in ad auctions"
    - "on the ratio of revenue to welfare in single parameter mechanism design"
    - "efficiency revenue trade offs in auctions"
    - "lower bounds on revenue of approximately optimal auctions"

Connected through hyperedge HE-2 (induced by source paper "approximating the revenue maximization problem with sharp demands"):
  with members:
    - "combinatorial walrasian equilibrium"
    - "independent set induced matching and pricing connections and tight subexponential time approximation hardnesses"
    - "on revenue maximization with sharp multi unit demands"
    - "graph products revisited tight approximation hardness of induced matching poset dimension and more"

Connected through hyperedge HE-3 (induced by source paper "on revenue maximization with sharp multi unit demands"):
  with members:
    - "approximating the revenue maximization problem with sharp demands"
    - "envy multi envy and revenue maximization"
    - "on profit maximizing pricing for the highway and tollbooth problems"

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)