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: "customer lifetime value prediction using embeddings"
  Abstract: We describe the Customer LifeTime Value (CLTV) prediction system deployed at ASOS.com, a global online fashion retailer. CLTV prediction is an important problem in e-commerce where an accurate estimate of future value allows retailers to effectively allocate marketing spend, identify and nurture high value customers and mitigate exposure to losses. The system at ASOS provides daily estimates of the future value of every customer and is one of the cornerstones of the personalised shopping experience. The state of the art in this domain uses large numbers of handcrafted features and ensemble regressors to forecast value, predict churn and evaluate customer loyalty. Recently, domains including language, vision and speech have shown dramatic advances by replacing handcrafted features with features that are learned automatically from data. We detail the system deployed at ASOS and show that learning feature representations is a promising extension to the state of the art in CLTV modelling. We propose a novel way to generate embeddings of customers, which addresses the issue of the ever changing product catalogue and obtain a significant improvement over an exhaustive set of handcrafted features.

Connected through hyperedge HE-1 (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-2 (induced by source paper "micro and macro level churn analysis of large scale mobile games"):
  with members:
    - "a semi supervised and inductive embedding model for churn prediction of large scale mobile games"
    - "streaming network embedding through local actions"
    - "learning edge representations via low rank asymmetric projections"
    - "semi supervised embedding in attributed networks with outliers"
    - "node2vec scalable feature learning for networks"
    - "revisiting semi supervised learning with graph embeddings"
    - "line large scale information network embedding"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "generating realistic sequences of customer level transactions for retail datasets"):
  with members:
    - "a survey of parallel sequential pattern mining"
    - "ecommercegan a generative adversarial network for e commerce"
    - "improved training of wasserstein gans"
    - "adam a method for stochastic optimization"
    - "distributed representations of sentences and documents"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-4 (induced by source paper "learning and transferring ids representation in e commerce"):
  with members:
    - "deep style match for complementary recommendation"
    - "item2vec neural item embedding for collaborative filtering"
    - "node2vec scalable feature learning for networks"
    - "joint embedding of hierarchical categories and entities for concept categorization and dataless classification"
    - "joint learning of the embedding of words and entities for named entity disambiguation"
    - "tensorflow a system for large scale machine learning"
    - "image based recommendations on styles and substitutes"
    - "line large scale information network embedding"

Connected through hyperedge HE-5 (induced by source paper "a recurrent neural network survival model predicting web user return time"):
  with members:
    - "deepar probabilistic forecasting with autoregressive recurrent networks"
    - "wide deep learning for recommender systems"
    - "on the properties of neural machine translation encoder decoder approaches"
    - "show and tell a neural image caption generator"
    - "sequence to sequence learning with neural networks"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-6 (induced by source paper "learning cheap and novel flight itineraries"):
  with members:
    - "deep embedding forest forest based serving with deep embedding features"
    - "related pins at pinterest the evolution of a real world recommender system"
    - "entity embeddings of categorical variables"
    - "deepcity a feature learning framework for mining location check ins"
    - "deepwalk online learning of social representations"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-7 (induced by source paper "a semi supervised and inductive embedding model for churn prediction of large scale mobile games"):
  with members:
    - "learning edge representations via low rank asymmetric projections"
    - "semi supervised embedding in attributed networks with outliers"
    - "node2vec scalable feature learning for networks"
    - "adam a method for stochastic optimization"
    - "deepwalk online learning of social representations"

Connected through hyperedge HE-8 (induced by source paper "customer lifetime value in video games using deep learning and parametric models"):
  with members:
    - "deep learning in neural networks"
    - "autonomous crm control via clv approximation with deep reinforcement learning in discrete and continuous action space"
    - "adam a method for stochastic optimization"
    - "going deeper with convolutions"

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.LG(Machine Learning)