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: "scalable semantic matching of queries to ads in sponsored search advertising"
  Abstract: Sponsored search represents a major source of revenue for web search engines. The advertising model brings a unique possibility for advertisers to target direct user intent communicated through a search query, usually done by displaying their ads alongside organic search results for queries deemed relevant to their products or services. However, due to a large number of unique queries, it is particularly challenging for advertisers to identify all relevant queries. For this reason search engines often provide a service of advanced matching, which automatically finds additional relevant queries for advertisers to bid on. We present a novel advance match approach based on the idea of semantic embeddings of queries and ads. The embeddings were learned using a large data set of user search sessions, consisting of search queries, clicked ads and search links, while utilizing contextual information such as dwell time and skipped ads. To address the large-scale nature of our problem, both in terms of data and vocabulary size, we propose a novel distributed algorithm for training of the embeddings. Finally, we present an approach for overcoming a cold-start problem associated with new ads and queries. We report results of editorial evaluation and online tests on actual search traffic. The results show that our approach significantly outperforms baselines in terms of relevance, coverage and incremental revenue. Lastly, as part of this study, we open sourced query embeddings that can be used to advance the field.

Connected through hyperedge HE-1 (induced by source paper "an image is worth a thousand features scalable product representations for in session type ahead personalization"):
  with members:
    - "personalized query auto completion through a lightweight representation of the user context"
    - "ctrl a conditional transformer language model for controllable generation"
    - "energy and policy considerations for deep learning in nlp"
    - "image based fashion product recommendation with deep learning"
    - "personalized language model for query auto completion"
    - "meta prod2vec product embeddings using side information for recommendation"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "e commerce in your inbox product recommendations at scale"

Connected through hyperedge HE-2 (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"
    - "optimized cost per click in taobao display advertising"
    - "sponsored search auctions with rich ads"
    - "mechanism design for value maximizers"
    - "fast core pricing for rich advertising auctions"
    - "smart pacing for effective online ad campaign optimization"

Connected through hyperedge HE-3 (induced by source paper "translator2vec understanding and representing human post editors"):
  with members:
    - "recurrent neural network based user authentication for freely typed keystroke data"
    - "wikiatomicedits a multilingual corpus of wikipedia edits for modeling language and discourse"
    - "learning to represent edits"
    - "marian fast neural machine translation in c"
    - "attention is all you need"
    - "a practical approach to dialogue response generation in closed domains"
    - "bag of tricks for efficient text classification"
    - "a convolutional encoder model for neural machine translation"

Connected through hyperedge HE-4 (induced by source paper "production ranking systems a review"):
  with members:
    - "beyond keywords and relevance a personalized ad retrieval framework in e commerce sponsored search"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "learning from multi user activity trails for b2b ad targeting"):
  with members:
    - "setexpan corpus based set expansion via context feature selection and rank ensemble"
    - "modelling customer online behaviours with neural networks applications to conversion prediction and advertising retargeting"
    - "e commerce in your inbox product recommendations at scale"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-6 (induced by source paper "lexical learning as an online optimal experiment building efficient search engines through human machine collaboration"):
  with members:
    - "practical optimal experiment design with probabilistic programs"

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)