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: "risk aware dynamic reserve prices of programmatic guarantee in display advertising"
  Abstract: Display advertising is an important online advertising type where banner advertisements (shortly ad) on websites are usually measured by how many times they are viewed by online users. There are two major channels to sell ad views. They can be auctioned off in real time or be directly sold through guaranteed contracts in advance. The former is also known as real-time bidding (RTB), in which media buyers come to a common marketplace to compete for a single ad view and this inventory will be allocated to a buyer in milliseconds by an auction model. Unlike RTB, buying and selling guaranteed contracts are not usually programmatic but through private negotiations as advertisers would like to customise their requests and purchase ad views in bulk. In this paper, we propose a simple model that facilitates the automation of direct sales. In our model, a media seller puts future ad views on sale and receives buy requests sequentially over time until the future delivery period. The seller maintains a hidden yet dynamically changing reserve price in order to decide whether to accept a buy request or not. The future supply and demand are assumed to be well estimated and static, and the model's revenue management is using inventory control theory where each computed reverse price is based on the updated supply and demand, and the unsold future ad views will be auctioned off in RTB to the meet the unfulfilled demand. The model has several desirable properties. First, it is not limited to the demand arrival assumption. Second, it will not affect the current equilibrium between RTB and direct sales as there are no posted guaranteed prices. Third, the model uses the expected revenue from RTB as a lower bound for inventory control and we show that a publisher can receive expected total revenue greater than or equal to those from only RTB if she uses the computed dynamic reserves prices for direct sales.

Connected through hyperedge HE-1 (induced by source paper "pricing average price advertising options when underlying spot market prices are discontinuous"):
  with members:
    - "a dynamic pricing model for unifying programmatic guarantee and real time bidding 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 "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"
    - "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 dynamic pricing model for unifying programmatic guarantee and real time bidding in display advertising"
    - "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-3 (induced by source paper "optimizing trade offs among stakeholders in real time bidding by incorporating multimedia metrics"):
  with members:
    - "a dynamic pricing model for unifying programmatic guarantee and real time bidding 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"

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)