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: "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.

Connected through hyperedge HE-1 (induced by source paper "bidding machine learning to bid for directly optimizing profits in display advertising"):
  with members:
    - "real time bidding by reinforcement learning in display advertising"
    - "display advertising with real time bidding rtb and behavioural targeting"
    - "product based neural networks for user response prediction"
    - "lift based bidding in ad selection"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "real time bidding by reinforcement learning in display advertising"):
  with members:
    - "display advertising with real time bidding rtb and behavioural targeting"
    - "smart pacing for effective online ad campaign optimization"
    - "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"
    - "sequential selection of correlated ads by pomdps"

Connected through hyperedge HE-3 (induced by source paper "managing risk of bidding in display advertising"):
  with members:
    - "display advertising with real time bidding rtb and behavioural targeting"
    - "real time bidding for online advertising measurement and analysis"
    - "budget optimization for sponsored search censored learning in mdps"
    - "a contextual bandit approach to personalized news article recommendation"

Connected through hyperedge HE-4 (induced by source paper "display advertising with real time bidding rtb and behavioural targeting"):
  with members:
    - "neural feature embedding for user response prediction in real time bidding rtb"
    - "optimal real time bidding frameworks discussion"
    - "product based neural networks for user response prediction"
    - "lift based bidding in ad selection"
    - "an empirical study on display ad impression viewability measurements"
    - "real time bidding benchmarking with ipinyou dataset"
    - "neural machine translation by jointly learning to align and translate"
    - "real time bid optimization with smooth budget delivery in online advertising"

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)