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: "e commerce in your inbox product recommendations at scale"
  Abstract: In recent years online advertising has become increasingly ubiquitous and effective. Advertisements shown to visitors fund sites and apps that publish digital content, manage social networks, and operate e-mail services. Given such large variety of internet resources, determining an appropriate type of advertising for a given platform has become critical to financial success. Native advertisements, namely ads that are similar in look and feel to content, have had great success in news and social feeds. However, to date there has not been a winning formula for ads in e-mail clients. In this paper we describe a system that leverages user purchase history determined from e-mail receipts to deliver highly personalized product ads to Yahoo Mail users. We propose to use a novel neural language-based algorithm specifically tailored for delivering effective product recommendations, which was evaluated against baselines that included showing popular products and products predicted based on co-occurrence. We conducted rigorous offline testing using a large-scale product purchase data set, covering purchases of more than 29 million users from 172 e-commerce websites. Ads in the form of product recommendations were successfully tested on online traffic, where we observed a steady 9% lift in click-through rates over other ad formats in mail, as well as comparable lift in conversion rates. Following successful tests, the system was launched into production during the holiday season of 2014.

Connected through hyperedge HE-1 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis"):
  with members:
    - "session based recommendation with graph neural network"
    - "contextual hybrid session based news recommendation with recurrent neural networks"
    - "on the importance of news content representation in hybrid neural session based recommender systems"
    - "deep learning based recommender system a survey and new perspectives"
    - "neurec on nonlinear transformation for personalized ranking"
    - "sequence aware recommender systems"
    - "evaluation of session based recommendation algorithms"
    - "multi pointer co attention networks for recommendation"

Connected through hyperedge HE-2 (induced by source paper "context aware recommendations for televisions using deep embeddings with relaxed n pairs loss objective"):
  with members:
    - "deep joint embeddings of context and content for recommendation"
    - "deep learning based recommender system a survey and new perspectives"
    - "grasp2vec learning object representations from self supervised grasping"
    - "deep metric learning with bier boosting independent embeddings robustly"
    - "the importance of context when recommending tv content dataset and algorithms"
    - "neural factorization machines for sparse predictive analytics"
    - "neural collaborative filtering"
    - "wide deep learning for recommender systems"

Connected through hyperedge HE-3 (induced by source paper "basconv aggregating heterogeneous interactions for basket recommendation with graph convolutional neural network"):
  with members:
    - "modeling complementary products and customer preferences with context knowledge for online recommendation"
    - "personalized bundle list recommendation"
    - "jscn joint spectral convolutional network for cross domain recommendation"
    - "neural graph collaborative filtering"
    - "graph convolutional neural networks for web scale recommender systems"
    - "complete the look scene based complementary product recommendation"
    - "spectral collaborative filtering"
    - "neural collaborative filtering"

Connected through hyperedge HE-4 (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"
    - "scalable semantic matching of queries to ads in sponsored search advertising"
    - "google s neural machine translation system bridging the gap between human and machine translation"

Connected through hyperedge HE-5 (induced by source paper "deep personalized re targeting"):
  with members:
    - "feature generation by convolutional neural network for click through rate prediction"
    - "large scale collaborative filtering with product embeddings"
    - "a survey on session based recommender systems"
    - "an attentive survey of attention models"
    - "predicting purchasing intent automatic feature learning using recurrent neural networks"
    - "deep neural network marketplace recommenders in online experiments"
    - "deep neural net with attention for multi channel multi touch attribution"
    - "a hierarchical bayesian model for size recommendation in fashion"

Connected through hyperedge HE-6 (induced by source paper "a large scale deep architecture for personalized grocery basket recommendations"):
  with members:
    - "complementary similarity learning using quadruplet network"
    - "ssdh semi supervised deep hashing for large scale image retrieval"
    - "modeling complementary products and customer preferences with context knowledge for online recommendation"
    - "complete the look scene based complementary product recommendation"
    - "inferring complementary products from baskets and browsing sessions"
    - "billion scale similarity search with gpus"
    - "item2vec neural item embedding for collaborative filtering"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-7 (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"
    - "scalable semantic matching of queries to ads in sponsored search advertising"
    - "bag of tricks for efficient text classification"

Connected through hyperedge HE-8 (induced by source paper "advanced customer activity prediction based on deep hierarchic encoder decoders"):
  with members:
    - "a long short demands aware model for next item recommendation"
    - "deep recommender engine based on efficient product embeddings neural pipeline"
    - "adversarial training of word2vec for basket completion"
    - "attention is all you need"
    - "fusing similarity models with markov chains for sparse sequential recommendation"
    - "improved recurrent neural networks for session based recommendations"
    - "session based recommendations with recurrent neural networks"
    - "a hierarchical recurrent encoder decoder for generative context aware query suggestion"

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)