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: "multi touch attribution based budget allocation in online advertising"
  Abstract: Budget allocation in online advertising deals with distributing the campaign (insertion order) level budgets to different sub-campaigns which employ different targeting criteria and may perform differently in terms of return-on-investment (ROI). In this paper, we present the efforts at Turn on how to best allocate campaign budget so that the advertiser or campaign-level ROI is maximized. To do this, it is crucial to be able to correctly determine the performance of sub-campaigns. This determination is highly related to the action-attribution problem, i.e. to be able to find out the set of ads, and hence the sub-campaigns that provided them to a user, that an action should be attributed to. For this purpose, we employ both last-touch (last ad gets all credit) and multi-touch (many ads share the credit) attribution methodologies. We present the algorithms deployed at Turn for the attribution problem, as well as their parallel implementation on the large advertiser performance datasets. We conclude the paper with our empirical comparison of last-touch and multi-touch attribution-based budget allocation in a real online advertising setting.

Connected through hyperedge HE-1 (induced by source paper "online joint bid daily budget optimization of internet advertising campaigns"):
  with members:
    - "bid optimization by multivariable control in display advertising"
    - "budget constrained bidding by model free reinforcement learning in display advertising"
    - "combinatorial semi bandits with knapsacks"
    - "display advertising with real time bidding rtb and behavioural targeting"
    - "feedback control of real time display advertising"
    - "combinatorial semi bandit with known covariance"
    - "thompson sampling for budgeted multi armed bandits"
    - "smart pacing for effective online ad campaign optimization"

Connected through hyperedge HE-2 (induced by source paper "learning multi touch conversion attribution with dual attention mechanisms for online advertising"):
  with members:
    - "bidding machine learning to bid for directly optimizing profits in display advertising"
    - "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"
    - "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-3 (induced by source paper "finding needle in a million metrics anomaly detection in a large scale computational advertising platform"):
  with members:
    - "online model evaluation in a large scale computational advertising platform"
    - "smart pacing for effective online ad campaign optimization"

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.AI(Artificial Intelligence)