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: "budget optimization for sponsored search censored learning in mdps"
  Abstract: We consider the budget optimization problem faced by an advertiser participating in repeated sponsored search auctions, seeking to maximize the number of clicks attained under that budget. We cast the budget optimization problem as a Markov Decision Process (MDP) with censored observations, and propose a learning algorithm based on the wellknown Kaplan-Meier or product-limit estimator. We validate the performance of this algorithm by comparing it to several others on a large set of search auction data from Microsoft adCenter, demonstrating fast convergence to optimal performance.

Connected through hyperedge HE-1 (induced by source paper "achieving fairness in the stochastic multi armed bandit problem"):
  with members:
    - "advancing subgroup fairness via sleeping experts"
    - "fair logistic regression an adversarial perspective"
    - "fairness in recommendation ranking through pairwise comparisons"
    - "combinatorial sleeping bandits with fairness constraints"
    - "policy learning for fairness in ranking"
    - "equal opportunity in online classification with partial feedback"
    - "introduction to multi armed bandits"
    - "online learning with an unknown fairness metric"

Connected through hyperedge HE-2 (induced by source paper "deep landscape forecasting for real time bidding advertising"):
  with members:
    - "bidding machine learning to bid for directly optimizing profits in display advertising"
    - "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"

Connected through hyperedge HE-3 (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-4 (induced by source paper "optimizing sponsored search ranking strategy by deep reinforcement learning"):
  with members:
    - "real time bidding by reinforcement learning in display advertising"
    - "deep reinforcement learning an overview"
    - "unbiased learning to rank with biased feedback"
    - "on line active reward learning for policy optimisation in spoken dialogue systems"
    - "asynchronous methods for deep reinforcement learning"
    - "a user simulator for task completion dialogues"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "continuous control with deep reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "thresholding at the monopoly price an agnostic way to improve bidding strategies in revenue maximizing auctions"):
  with members:
    - "dynamic reserve prices for repeated auctions learning from bids"
    - "learning to bid in revenue maximizing auctions"
    - "online learning for measuring incentive compatibility in ad auctions"
    - "explicit shading strategies for repeated truthful auctions"
    - "thresholding the virtual value a simple method to increase welfare and lower reserve prices in online auction systems"
    - "a field guide to personalized reserve prices"
    - "learning simple auctions"
    - "sequential mechanisms with ex post participation guarantees"

Connected through hyperedge HE-6 (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"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "explicit shading strategies for repeated truthful auctions"):
  with members:
    - "dynamic reserve prices for repeated auctions learning from bids"
    - "a field guide to personalized reserve prices"
    - "learning simple auctions"
    - "how to manipulate truthful prior dependent mechanisms"
    - "the sample complexity of revenue maximization"
    - "online learning in repeated auctions"
    - "learning in auctions regret is hard envy is easy"
    - "learning valuation distributions from partial observation"

Connected through hyperedge HE-8 (induced by source paper "deep reinforcement learning for sponsored search real time bidding"):
  with members:
    - "real time bidding by reinforcement learning in display advertising"
    - "ladder a human level bidding agent for large scale real time online auctions"
    - "continuous deep q learning with model based acceleration"
    - "multiagent cooperation and competition with deep reinforcement learning"
    - "real time bidding for online advertising measurement and analysis"
    - "playing atari with deep reinforcement learning"
    - "real time bid optimization with smooth budget delivery in online advertising"
    - "budget optimization in search based advertising auctions"

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)