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: "beyond keywords and relevance a personalized ad retrieval framework in e commerce sponsored search"
  Abstract: In most sponsored search platforms, advertisers bid on some keywords for their advertisements (ads). Given a search request, ad retrieval module rewrites the query into bidding keywords, and uses these keywords as keys to select Top N ads through inverted indexes. In this way, an ad will not be retrieved even if queries are related when the advertiser does not bid on corresponding keywords. Moreover, most ad retrieval approaches regard rewriting and ad-selecting as two separated tasks, and focus on boosting relevance between search queries and ads. Recently, in e-commerce sponsored search more and more personalized information has been introduced, such as user profiles, long-time and real-time clicks. Personalized information makes ad retrieval able to employ more elements (e.g. real-time clicks) as search signals and retrieval keys, however it makes ad retrieval more difficult to measure ads retrieved through different signals.#R##N##R##N#To address these problems, we propose a novel ad retrieval framework beyond keywords and relevance in e-commerce sponsored search. Firstly, we employ historical ad click data to initialize a hierarchical network representing signals, keys and ads, in which personalized information is introduced. Then we train a model on top of the hierarchical network by learning the weights of edges. Finally we select the best edges according to the model, boosting RPM/CTR. Experimental results on our e-commerce platform demonstrate that our ad retrieval framework achieves good performance.

Connected through hyperedge HE-1 (induced by source paper "aiads automated and intelligent advertising system for sponsored search"):
  with members:
    - "deep reinforcement learning for sponsored search real time bidding"
    - "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-2 (induced by source paper "production ranking systems a review"):
  with members:
    - "practical constrained optimization of auction mechanisms in e commerce sponsored search advertising"
    - "eenmf an end to end neural matching framework for e commerce sponsored search"
    - "deep learning inference in facebook data centers characterization performance optimizations and hardware implications"
    - "billion scale commodity embedding for e commerce recommendation in alibaba"
    - "ann benchmarks a benchmarking tool for approximate nearest neighbor algorithms"
    - "visually aware fashion recommendation and design with generative image models"
    - "pixie a system for recommending 3 billion items to 200 million users in real time"
    - "scalable semantic matching of queries to ads in sponsored search advertising"

Connected through hyperedge HE-3 (induced by source paper "eenmf an end to end neural matching framework for e commerce sponsored search"):
  with members:
    - "self normalizing neural networks"
    - "billion scale similarity search with gpus"
    - "an overview of multi task learning in deep neural networks"
    - "swish a self gated activation function"
    - "wide deep learning for recommender systems"
    - "learning to match using local and distributed representations of text for web search"
    - "deep sentence embedding using long short term memory networks analysis and application to information retrieval"
    - "text matching as image recognition"

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.IR(Information Retrieval)