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: "bidding for representative allocations for display advertising"
  Abstract: Display advertising has traditionally been sold via guaranteed contracts -- a guaranteed contract is a deal between a publisher and an advertiser to allocate a certain number of impressions over a certain period, for a pre-specified price per impression. However, as spot markets for display ads, such as the RightMedia Exchange, have grown in prominence, the selection of advertisements to show on a given page is increasingly being chosen based on price, using an auction. As the number of participants in the exchange grows, the price of an impressions becomes a signal of its value. This correlation between price and value means that a seller implementing the contract through bidding should offer the contract buyer a range of prices, and not just the cheapest impressions necessary to fulfill its demand. #R##N#Implementing a contract using a range of prices, is akin to creating a mutual fund of advertising impressions, and requires {\em randomized bidding}. We characterize what allocations can be implemented with randomized bidding, namely those where the desired share obtained at each price is a non-increasing function of price. In addition, we provide a full characterization of when a set of campaigns are compatible and how to implement them with randomized bidding strategies.

Connected through hyperedge HE-1 (induced by source paper "toward controlling discrimination in online ad auctions"):
  with members:
    - "discrimination through optimization how facebook s ad delivery can lead to biased outcomes"
    - "fair and diverse dpp based data summarization"
    - "classification with fairness constraints a meta algorithm with provable guarantees"
    - "ranking with fairness constraints"
    - "multiwinner voting with fairness constraints"
    - "reducing controversy by connecting opposing views"
    - "discrimination in online ad delivery"

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 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"

Connected through hyperedge HE-4 (induced by source paper "risk aware dynamic reserve prices of programmatic guarantee in display advertising"):
  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"
    - "multi keyword multi click advertisement option contracts for sponsored search"
    - "shale an efficient algorithm for allocation of guaranteed display advertising"

Connected through hyperedge HE-5 (induced by source paper "efficient delivery policy to minimize user traffic consumption in guaranteed advertising"):
  with members:
    - "a dynamic pricing model for unifying programmatic guarantee and real time bidding in display advertising"
    - "shale an efficient algorithm for allocation of guaranteed display advertising"
    - "ad serving using a compact allocation plan"
    - "online stochastic matching beating 1 1 e"

Connected through hyperedge HE-6 (induced by source paper "online budgeted allocation with general budgets"):
  with members:
    - "fast algorithms for online stochastic convex programming"
    - "shale an efficient algorithm for allocation of guaranteed display advertising"
    - "online vertex weighted bipartite matching and single bid budgeted allocations"

Connected through hyperedge HE-7 (induced by source paper "statistical arbitrage mining for display advertising"):
  with members:
    - "a dynamic pricing model for unifying programmatic guarantee and real time bidding in display advertising"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "fast algorithms for online stochastic convex programming"):
  with members:
    - "near optimal online algorithms and fast approximation algorithms for resource allocation problems"
    - "bandits with concave rewards and convex knapsacks"
    - "how the experts algorithm can help solve lps online"
    - "a near optimal dynamic learning algorithm for online matching problems with concave returns"
    - "bandits with knapsacks"
    - "ad serving using a compact allocation plan"
    - "dynamic pricing with limited supply"
    - "online vertex weighted bipartite matching and single bid budgeted allocations"

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.MA(Multiagent Systems)