human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "statistical arbitrage mining for display advertising"):
  Abstract: We study and formulate arbitrage in display advertising. Real-Time Bidding (RTB) mimics stock spot exchanges and utilises computers to algorithmically buy display ads per impression via a real-time auction. Despite the new automation, the ad markets are still informationally inefficient due to the heavily fragmented marketplaces. Two display impressions with similar or identical effectiveness (e.g., measured by conversion or click-through rates for a targeted audience) may sell for quite different prices at different market segments or pricing schemes. In this paper, we propose a novel data mining paradigm called Statistical Arbitrage Mining (SAM) focusing on mining and exploiting price discrepancies between two pricing schemes. In essence, our SAMer is a meta-bidder that hedges advertisers' risk between CPA (cost per action)-based campaigns and CPM (cost per mille impressions)-based ad inventories; it statistically assesses the potential profit and cost for an incoming CPM bid request against a portfolio of CPA campaigns based on the estimated conversion rate, bid landscape and other statistics learned from historical data. In SAM, (i) functional optimisation is utilised to seek for optimal bidding to maximise the expected arbitrage net profit, and (ii) a portfolio-based risk management solution is leveraged to reallocate bid volume and budget across the set of campaigns to make a risk and return trade-off. We propose to jointly optimise both components in an EM fashion with high efficiency to help the meta-bidder successfully catch the transient statistical arbitrage opportunities in RTB. Both the offline experiments on a real-world large-scale dataset and online A/B tests on a commercial platform demonstrate the effectiveness of our proposed solution in exploiting arbitrage in various model settings and market environments.

Includes members:
  Member "a dynamic pricing model for unifying programmatic guarantee and real time bidding in display advertising":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "pricing average price advertising options when underlying spot market prices are discontinuous")
  Member "real time bidding benchmarking with ipinyou dataset":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "scalable bid landscape forecasting in real time bidding")
  Member "real time bidding for online advertising measurement and analysis":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "motiac multi objective actor critics for real time bidding")
  Member "real time bid optimization with smooth budget delivery in online advertising":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "online joint bid daily budget optimization of internet advertising campaigns")
  Member "budget optimization for sponsored search censored learning in mdps":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "achieving fairness in the stochastic multi armed bandit problem")
  Member "unbiased offline evaluation of contextual bandit based news article recommendation algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "self supervised contextual bandits in computer vision")
  Member "online stochastic packing applied to display ad allocation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "near optimal online algorithms and fast approximation algorithms for resource allocation problems")
  Member "bidding for representative allocations for display advertising":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "toward controlling discrimination in online ad auctions")

Question: Please tell me which class the source paper 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)