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: "a dynamic pricing model for unifying programmatic guarantee and real time bidding in display advertising"
  Abstract: There are two major ways of selling impressions in display advertising. They are either sold in spot through auction mechanisms or in advance via guaranteed contracts. The former has achieved a significant automation via real-time bidding (RTB); however, the latter is still mainly done over the counter through direct sales. This paper proposes a mathematical model that allocates and prices the future impressions between real-time auctions and guaranteed contracts. Under conventional economic assumptions, our model shows that the two ways can be seamless combined programmatically and the publisher's revenue can be maximized via price discrimination and optimal allocation. We consider advertisers are risk-averse, and they would be willing to purchase guaranteed impressions if the total costs are less than their private values. We also consider that an advertiser's purchase behavior can be affected by both the guaranteed price and the time interval between the purchase time and the impression delivery date. Our solution suggests an optimal percentage of future impressions to sell in advance and provides an explicit formula to calculate at what prices to sell. We find that the optimal guaranteed prices are dynamic and are non-decreasing over time. We evaluate our method with RTB datasets and find that the model adopts different strategies in allocation and pricing according to the level of competition. From the experiments we find that, in a less competitive market, lower prices of the guaranteed contracts will encourage the purchase in advance and the revenue gain is mainly contributed by the increased competition in future RTB. In a highly competitive market, advertisers are more willing to purchase the guaranteed contracts and thus higher prices are expected. The revenue gain is largely contributed by the guaranteed selling.

Connected through hyperedge HE-1 (induced by source paper "pricing average price advertising options when underlying spot market prices are discontinuous"):
  with members:
    - "risk aware dynamic reserve prices of programmatic guarantee in display advertising"
    - "a lattice framework for pricing display advertisement options with the stochastic volatility underlying model"
    - "real time bidding for online advertising measurement and analysis"
    - "multi keyword multi click advertisement option contracts for sponsored search"
    - "shale an efficient algorithm for allocation of guaranteed display advertising"
    - "sequential selection of correlated ads by pomdps"

Connected through hyperedge HE-2 (induced by source paper "a multi agent reinforcement learning method for impression allocation in online display advertising"):
  with members:
    - "mean field multi agent reinforcement learning"
    - "budget constrained bidding by model free reinforcement learning in display advertising"
    - "counterfactual multi agent policy gradients"
    - "real time bidding by reinforcement learning in display advertising"
    - "emergence of grounded compositional language in multi agent populations"
    - "multi agent actor critic for mixed cooperative competitive environments"
    - "learning to communicate with deep multi agent reinforcement learning"
    - "feedback control of real time display advertising"

Connected through hyperedge HE-3 (induced by source paper "combining guaranteed and spot markets in display advertising selling guaranteed page views with stochastic demand"):
  with members:
    - "pricing average price advertising options when underlying spot market prices are discontinuous"
    - "risk aware dynamic reserve prices of programmatic guarantee in display advertising"
    - "ad exchange envy free auctions with mediators"
    - "efficient delivery policy to minimize user traffic consumption in guaranteed advertising"
    - "combining spot and futures markets a hybrid market approach to dynamic spectrum access"
    - "a lattice framework for pricing display advertisement options with the stochastic volatility underlying model"
    - "multi keyword multi click advertisement option contracts for sponsored search"
    - "shale an efficient algorithm for allocation of guaranteed display advertising"

Connected through hyperedge HE-4 (induced by source paper "optimizing trade offs among stakeholders in real time bidding by incorporating multimedia metrics"):
  with members:
    - "risk aware dynamic reserve prices of programmatic guarantee in display advertising"
    - "optimising trade offs among stakeholders in ad auctions"
    - "a lattice framework for pricing display advertisement options with the stochastic volatility underlying model"
    - "ranking and tradeoffs in sponsored search auctions"
    - "multi keyword multi click advertisement option contracts for sponsored search"

Connected through hyperedge HE-5 (induced by source paper "risk aware dynamic reserve prices of programmatic guarantee in display advertising"):
  with members:
    - "a lattice framework for pricing display advertisement options with the stochastic volatility underlying model"
    - "multi keyword multi click advertisement option contracts for sponsored search"
    - "shale an efficient algorithm for allocation of guaranteed display advertising"
    - "bidding for representative allocations for display advertising"

Connected through hyperedge HE-6 (induced by source paper "efficient delivery policy to minimize user traffic consumption in guaranteed advertising"):
  with members:
    - "shale an efficient algorithm for allocation of guaranteed display advertising"
    - "ad serving using a compact allocation plan"
    - "bidding for representative allocations for display advertising"
    - "online stochastic matching beating 1 1 e"

Connected through hyperedge HE-7 (induced by source paper "statistical arbitrage mining for display advertising"):
  with members:
    - "real time bidding benchmarking with ipinyou dataset"
    - "real time bidding for online advertising measurement and analysis"
    - "real time bid optimization with smooth budget delivery in online advertising"
    - "budget optimization for sponsored search censored learning in mdps"
    - "unbiased offline evaluation of contextual bandit based news article recommendation algorithms"
    - "online stochastic packing applied to display ad allocation"
    - "bidding for representative allocations for display advertising"

Connected through hyperedge HE-8 (induced by source paper "a lattice framework for pricing display ad options with the stochastic volatility underlying model"):
  with members:
    - "real time bidding for online advertising measurement and analysis"
    - "multi keyword multi click advertisement option contracts for sponsored search"
    - "sequential selection of correlated ads by pomdps"

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)