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 machine learning to bid for directly optimizing profits in display advertising"
  Abstract: Real-time bidding (RTB) based display advertising has become one of the key technological advances in computational advertising. RTB enables advertisers to buy individual ad impressions via an auction in real-time and facilitates the evaluation and the bidding of individual impressions across multiple advertisers. In RTB, the advertisers face three main challenges when optimizing their bidding strategies, namely (i) estimating the utility (e.g., conversions, clicks) of the ad impression, (ii) forecasting the market value (thus the cost) of the given ad impression, and (iii) deciding the optimal bid for the given auction based on the first two. Previous solutions assume the first two are solved before addressing the bid optimization problem. However, these challenges are strongly correlated and dealing with any individual problem independently may not be globally optimal. In this paper, we propose  Bidding Machine , a comprehensive  learning to bid  framework, which consists of three optimizers dealing with each challenge above, and as a whole, jointly optimizes these three parts. We show that such a joint optimization would largely increase the campaign effectiveness and the profit. From the learning perspective, we show that the bidding machine can be updated smoothly with both offline periodical batch or online sequential training schemes. Our extensive offline empirical study and online A/B testing verify the high effectiveness of the proposed bidding machine.

Connected through hyperedge HE-1 (induced by source paper "sequential recommendation with dual side neighbor based collaborative relation modeling"):
  with members:
    - "lifelong sequential modeling with personalized memorization for user response prediction"
    - "dual graph attention networks for deep latent representation of multifaceted social effects in recommender systems"
    - "session based social recommendation via dynamic graph attention networks"
    - "deep learning based recommender system a survey and new perspectives"
    - "outer product based neural collaborative filtering"
    - "repeatnet a repeat aware neural recommendation machine for session based recommendation"
    - "recurrent neural networks for long and short term sequential recommendation"
    - "recurrent neural networks with top k gains for session based recommendations"

Connected through hyperedge HE-2 (induced by source paper "lifelong sequential modeling with personalized memorization for user response prediction"):
  with members:
    - "on training recurrent neural networks for lifelong learning"
    - "collaborative memory network for recommendation systems"
    - "learning multi touch conversion attribution with dual attention mechanisms for online advertising"
    - "next item recommendation with self attention"
    - "recurrent neural networks for long and short term sequential recommendation"
    - "recurrent neural networks with top k gains for session based recommendations"
    - "personalized top n sequential recommendation via convolutional sequence embedding"
    - "self attentive sequential recommendation"

Connected through hyperedge HE-3 (induced by source paper "deep landscape forecasting for real time bidding advertising"):
  with members:
    - "sliced recurrent neural networks"
    - "deep learning for patient specific kidney graft survival analysis"
    - "display advertising with real time bidding rtb and behavioural targeting"
    - "product based neural networks for user response prediction"
    - "architectural complexity measures of recurrent neural networks"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "real time bidding benchmarking with ipinyou dataset"
    - "real time bidding for online advertising measurement and analysis"

Connected through hyperedge HE-4 (induced by source paper "bid optimization by multivariable control in display advertising"):
  with members:
    - "budget constrained bidding by model free reinforcement learning in display advertising"
    - "real time bidding by reinforcement learning in display advertising"
    - "feedback control of real time display advertising"
    - "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"
    - "a dynamic near optimal algorithm for online linear programming"

Connected through hyperedge HE-5 (induced by source paper "learning multi touch conversion attribution with dual attention mechanisms for online advertising"):
  with members:
    - "a dual stage attention based recurrent neural network for time series prediction"
    - "convolutional sequence to sequence learning"
    - "deep learning over multi field categorical data a case study on user response prediction"
    - "display advertising with real time bidding rtb and behavioural targeting"
    - "multi touch attribution based budget allocation in online advertising"
    - "lift based bidding in ad selection"
    - "sequential click prediction for sponsored search with recurrent neural networks"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-6 (induced by source paper "the second price knapsack problem near optimal real time bidding in internet advertisement"):
  with members:
    - "multiplicative pacing equilibria in auction markets"
    - "fast algorithms for online stochastic convex programming"
    - "real time bidding benchmarking with ipinyou dataset"
    - "bandits with knapsacks"
    - "real time bidding for online advertising measurement and analysis"
    - "real time bid optimization with smooth budget delivery in online advertising"
    - "online stochastic packing applied to display ad allocation"
    - "a dynamic near optimal algorithm for online linear programming"

Connected through hyperedge HE-7 (induced by source paper "cia towards a unified marketing optimization framework for e commerce sponsored search"):
  with members:
    - "optimized cost per click in taobao display advertising"
    - "real time bidding by reinforcement learning in display advertising"
    - "ladder a human level bidding agent for large scale real time online auctions"
    - "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)