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: "optimized cost per click in taobao display advertising"
  Abstract: Taobao, as the largest online retail platform in the world, provides billions of online display advertising impressions for millions of advertisers every day. For commercial purposes, the advertisers bid for specific spots and target crowds to compete for business traffic. The platform chooses the most suitable ads to display in tens of milliseconds. Common pricing methods include cost per mille (CPM) and cost per click (CPC). Traditional advertising systems target certain traits of users and ad placements with fixed bids, essentially regarded as coarse-grained matching of bid and traffic quality. However, the fixed bids set by the advertisers competing for different quality requests cannot fully optimize the advertisers' key requirements. Moreover, the platform has to be responsible for the business revenue and user experience. Thus, we proposed a bid optimizing strategy called optimized cost per click (OCPC) which automatically adjusts the bid to achieve finer matching of bid and traffic quality of page view (PV) request granularity. Our approach optimizes advertisers' demands, platform business revenue and user experience and as a whole improves traffic allocation efficiency. We have validated our approach in Taobao display advertising system in production. The online A/B test shows our algorithm yields substantially better results than previous fixed bid manner.

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"
    - "deep reinforcement learning for sponsored search real time bidding"
    - "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"
    - "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 "safe scalable automatic feature engineering framework for industrial tasks"):
  with members:
    - "interpretable mtl from heterogeneous domains using boosted tree"
    - "autoint automatic feature interaction learning via self attentive neural networks"
    - "automl a survey of the state of the art"
    - "survey on automated machine learning"
    - "taking human out of learning applications a survey on automated machine learning"
    - "feature engineering for predictive modeling using reinforcement learning"
    - "deep cross network for ad click predictions"
    - "one button machine for automating feature engineering in relational databases"

Connected through hyperedge HE-3 (induced by source paper "conversion rate prediction via post click behaviour modeling"):
  with members:
    - "bert4rec sequential recommendation with bidirectional encoder representations from transformer"
    - "behavior sequence transformer for e commerce recommendation in alibaba"
    - "sdm sequential deep matching model for online large scale recommender system"
    - "deep session interest network for click through rate prediction"
    - "deep forest"
    - "rank and rate multi task learning for recommender systems"
    - "deep forest towards an alternative to deep neural networks"
    - "click through rate prediction for contextual advertisment using linear regression"

Connected through hyperedge HE-4 (induced by source paper "joint optimization of tree based index and deep model for recommender systems"):
  with members:
    - "practice on long sequential user behavior modeling for click through rate prediction"
    - "deep learning based recommender system a survey and new perspectives"
    - "xdeepfm combining explicit and implicit feature interactions for recommender systems"
    - "the case for learned index structures"
    - "neural collaborative filtering"
    - "billion scale similarity search with gpus"
    - "wide deep learning for recommender systems"
    - "ups and downs modeling the visual evolution of fashion trends with one class collaborative filtering"

Connected through hyperedge HE-5 (induced by source paper "aiads automated and intelligent advertising system for sponsored search"):
  with members:
    - "deep reinforcement learning for sponsored search real time bidding"
    - "beyond keywords and relevance a personalized ad retrieval framework in e commerce sponsored search"
    - "roi constrained auctions"
    - "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-6 (induced by source paper "estimating individual advertising effect in e commerce"):
  with members:
    - "gsp the cinderella of mechanism design"
    - "combining observational and experimental data to find heterogeneous treatment effects"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "cia towards a unified marketing optimization framework for e commerce sponsored search"):
  with members:
    - "bidding machine learning to bid for directly optimizing profits in 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"

Connected through hyperedge HE-8 (induced by source paper "beyond keywords and relevance a personalized ad retrieval framework in e commerce sponsored search"):
  with members:
    - "a deep relevance matching model for ad hoc retrieval"
    - "a hierarchical recurrent encoder decoder for generative context aware query suggestion"
    - "simrank query rewriting through link analysis of the click graph"

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)