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: "deep reinforcement learning for sponsored search real time bidding"
  Abstract: Bidding optimization is one of the most critical problems in online advertising. Sponsored search (SS) auction, due to the randomness of user query behavior and platform nature, usually adopts keyword-level bidding strategies. In contrast, the display advertising (DA), as a relatively simpler scenario for auction, has taken advantage of real-time bidding (RTB) to boost the performance for advertisers. In this paper, we consider the RTB problem in sponsored search auction, named SS-RTB. SS-RTB has a much more complex dynamic environment, due to stochastic user query behavior and more complex bidding policies based on multiple keywords of an ad. Most previous methods for DA cannot be applied. We propose a reinforcement learning (RL) solution for handling the complex dynamic environment. Although some RL methods have been proposed for online advertising, they all fail to address the "environment changing" problem: the state transition probabilities vary between two days. Motivated by the observation that auction sequences of two days share similar transition patterns at a proper aggregation level, we formulate a robust MDP model at hour-aggregation level of the auction data and propose a control-by-model framework for SS-RTB. Rather than generating bid prices directly, we decide a bidding model for impressions of each hour and perform real-time bidding accordingly. We also extend the method to handle the multi-agent problem. We deployed the SS-RTB system in the e-commerce search auction platform of Alibaba. Empirical experiments of offline evaluation and online A/B test demonstrate the effectiveness of our method.

Connected through hyperedge HE-1 (induced by source paper "motiac multi objective actor critics for real time bidding"):
  with members:
    - "a generalized algorithm for multi objective reinforcement learning and policy adaptation"
    - "dynamic weights in multi objective deep reinforcement learning"
    - "a nonparametric delayed feedback model for conversion rate prediction"
    - "budget constrained bidding by model free reinforcement learning in display advertising"
    - "multi reward reinforced summarization with saliency and entailment"
    - "optimized cost per click in taobao display advertising"
    - "hybrid reward architecture for reinforcement learning"
    - "deepfm a factorization machine based neural network for ctr prediction"

Connected through hyperedge HE-2 (induced by source paper "deep reinforcement learning for search recommendation and online advertising a survey by xiangyu zhao long xia jiliang tang and dawei yin with martin vesely as coordinator"):
  with members:
    - "model based reinforcement learning for whole chain recommendations"
    - "learning to coordinate multiple reinforcement learning agents for diverse query reformulation"
    - "learning to collaborate multi scenario ranking via multi agent reinforcement learning"
    - "recommendations with negative feedback via pairwise deep reinforcement learning"
    - "deep reinforcement learning for page wise recommendations"
    - "a multi agent reinforcement learning method for impression allocation in online display advertising"
    - "learning to advertise with adaptive exposure via constrained two level reinforcement learning"
    - "learning contextual bandits in a non stationary environment"

Connected through hyperedge HE-3 (induced by source paper "aiads automated and intelligent advertising system for sponsored search"):
  with members:
    - "beyond keywords and relevance a personalized ad retrieval framework in e commerce sponsored search"
    - "roi constrained auctions"
    - "optimized cost per click in taobao display advertising"
    - "sponsored search auctions with rich ads"
    - "mechanism design for value maximizers"
    - "scalable semantic matching of queries to ads in sponsored search advertising"
    - "fast core pricing for rich advertising auctions"
    - "smart pacing for effective online ad campaign optimization"

Connected through hyperedge HE-4 (induced by source paper "infer your enemies and know yourself learning in real time bidding with partially observable opponents"):
  with members:
    - "budget constrained bidding by model free reinforcement learning in display advertising"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "deep recurrent survival analysis"
    - "attention is all you need"
    - "real time bidding by reinforcement learning in display advertising"
    - "opponent modeling in deep reinforcement learning"
    - "continuous control with deep reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "personalized attraction enhanced sponsored search with multi task learning"):
  with members:
    - "a survey on multi task learning"
    - "an overview of multi task learning in deep neural networks"
    - "learning multiple tasks with deep relationship networks"
    - "modeling attractiveness and multiple clicks in sponsored search results"
    - "truthful learning mechanisms for multi slot sponsored search auctions with externalities"
    - "distributed representations of words and phrases and their compositionality"
    - "budget optimization in search based advertising auctions"

Connected through hyperedge HE-6 (induced by source paper "learning to advertise with adaptive exposure via constrained two level reinforcement learning"):
  with members:
    - "learning to collaborate multi scenario ranking via multi agent reinforcement learning"
    - "reward constrained policy optimization"
    - "reinforcement learning to rank in e commerce search engine formalization analysis and application"
    - "budget constrained bidding by model free reinforcement learning in display advertising"
    - "hindsight experience replay"
    - "reinforcement mechanism design for e commerce"
    - "real time bidding by reinforcement learning in display advertising"
    - "constrained policy optimization"

Connected through hyperedge HE-7 (induced by source paper "reinforcement learning for online information seeking"):
  with members:
    - "model based reinforcement learning for whole chain recommendations"
    - "learning to coordinate multiple reinforcement learning agents for diverse query reformulation"
    - "learning to collaborate multi scenario ranking via multi agent reinforcement learning"
    - "a multi agent reinforcement learning method for impression allocation in online display advertising"
    - "learning to advertise with adaptive exposure via constrained two level reinforcement learning"
    - "learning contextual bandits in a non stationary environment"
    - "reinforcement learning to rank in e commerce search engine formalization analysis and application"
    - "budget constrained bidding by model free reinforcement learning in display advertising"

Connected through hyperedge HE-8 (induced by source paper "learning adaptive display exposure for real time advertising"):
  with members:
    - "reward constrained policy optimization"
    - "reinforcement learning to rank in e commerce search engine formalization analysis and application"
    - "budget constrained bidding by model free reinforcement learning in display advertising"
    - "hindsight experience replay"
    - "reinforcement mechanism design for e commerce"
    - "real time bidding by reinforcement learning in display advertising"
    - "constrained policy optimization"
    - "the option critic architecture"

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)