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: "large scale collaborative filtering with product embeddings"
  Abstract: The application of machine learning techniques to large-scale personalized recommendation problems is a challenging task. Such systems must make sense of enormous amounts of implicit feedback in order to understand user preferences across numerous product categories. This paper presents a deep learning based solution to this problem within the collaborative filtering with implicit feedback framework. Our approach combines neural attention mechanisms, which allow for context dependent weighting of past behavioral signals, with representation learning techniques to produce models which obtain extremely high coverage, can easily incorporate new information as it becomes available, and are computationally efficient. Offline experiments demonstrate significant performance improvements when compared to several alternative methods from the literature. Results from an online setting show that the approach compares favorably with current production techniques used to produce personalized product recommendations.

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"
    - "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"
    - "word2vec applied to recommendation hyperparameters matter"

Connected through hyperedge HE-2 (induced by source paper "a simple deep personalized recommendation system"):
  with members:
    - "session based recommendation with graph neural network"
    - "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"
    - "word2vec applied to recommendation hyperparameters matter"
    - "e commerce in your inbox product recommendations at scale"
    - "distributed representations of words and phrases and their compositionality"

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)