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: "product based neural networks for user response prediction over multi field categorical data"
  Abstract: User response prediction is a crucial component for personalized information retrieval and filtering scenarios, such as recommender system and web search. The data in user response prediction is mostly in a multi-field categorical format and transformed into sparse representations via one-hot encoding. Due to the sparsity problems in representation and optimization, most research focuses on feature engineering and shallow modeling. Recently, deep neural networks have attracted research attention on such a problem for their high capacity and end-to-end training scheme. In this paper, we study user response prediction in the scenario of click prediction. We first analyze a coupled gradient issue in latent vector-based models and propose kernel product to learn field-aware feature interactions. Then we discuss an insensitive gradient issue in DNN-based models and propose Product-based Neural Network (PNN) which adopts a feature extractor to explore feature interactions. Generalizing the kernel product to a net-in-net architecture, we further propose Product-network In Network (PIN) which can generalize previous models. Extensive experiments on 4 industrial datasets and 1 contest dataset demonstrate that our models consistently outperform 8 baselines on both AUC and log loss. Besides, PIN makes great CTR improvement (relatively 34.67%) in online A/B test.

Connected through hyperedge HE-1 (induced by source paper "multi graph convolution collaborative filtering"):
  with members:
    - "feature generation by convolutional neural network for click through rate prediction"
    - "graph convolutional neural networks for web scale recommender systems"
    - "a graph cnn for 3d point cloud classification"
    - "xdeepfm combining explicit and implicit feature interactions for recommender systems"
    - "deepfm a factorization machine based neural network for ctr prediction"
    - "birank towards ranking on bipartite graphs"
    - "wide deep learning for recommender systems"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "fi gnn modeling feature interactions via graph neural networks for ctr prediction"):
  with members:
    - "session based recommendation with graph neural network"
    - "autoint automatic feature interaction learning via self attentive neural networks"
    - "semi supervised compatibility learning across categories for clothing matching"
    - "dressing as a whole outfit compatibility learning based on node wise graph neural networks"
    - "a comprehensive survey on graph neural networks"
    - "graph neural networks a review of methods and applications"
    - "graph to sequence learning using gated graph neural networks"
    - "xdeepfm combining explicit and implicit feature interactions for recommender systems"

Connected through hyperedge HE-3 (induced by source paper "modeling embedding dimension correlations via convolutional neural collaborative filtering"):
  with members:
    - "temporal relational ranking for stock prediction"
    - "mmalfm explainable recommendation by leveraging reviews and images"
    - "fast matrix factorization with non uniform weights on missing data"
    - "latent relational metric learning via memory based attention for collaborative ranking"
    - "outer product based neural collaborative filtering"
    - "adversarial personalized ranking for recommendation"
    - "a simple convolutional generative network for next item recommendation"
    - "attentive long short term preference modeling for personalized product search"

Connected through hyperedge HE-4 (induced by source paper "feature generation by convolutional neural network for click through rate prediction"):
  with members:
    - "deep learning based recommender system a survey and new perspectives"
    - "deepfm an end to end wide deep learning framework for ctr prediction"
    - "xdeepfm combining explicit and implicit feature interactions for recommender systems"
    - "learning to generate reviews and discovering sentiment"
    - "neural factorization machines for sparse predictive analytics"
    - "attention is all you need"
    - "failures of gradient based deep learning"
    - "deep cross network for ad click predictions"

Connected through hyperedge HE-5 (induced by source paper "an end to end neighborhood based interaction model for knowledge enhanced recommendation"):
  with members:
    - "graph convolutional neural networks for web scale recommender systems"
    - "ripplenet propagating user preferences on the knowledge graph for recommender systems"
    - "kb4rec a dataset for linking knowledge bases with recommender systems"
    - "fastgcn fast learning with graph convolutional networks via importance sampling"
    - "nais neural attentive item similarity model for recommendation"
    - "graphgan graph representation learning with generative adversarial nets"
    - "deepfm a factorization machine based neural network for ctr prediction"
    - "wide deep learning for recommender systems"

Connected through hyperedge HE-6 (induced by source paper "cascading association augmented sequential recommendation"):
  with members:
    - "a sequential embedding approach for item recommendation with heterogeneous attributes"
    - "translation based recommendation"
    - "node2vec scalable feature learning for networks"
    - "sca cnn spatial and channel wise attention in convolutional networks for image captioning"
    - "semi supervised classification with graph convolutional networks"
    - "session based recommendations with recurrent neural networks"
    - "line large scale information network embedding"
    - "deepwalk online learning of social representations"

Connected through hyperedge HE-7 (induced by source paper "flen leveraging field for scalable ctr prediction"):
  with members:
    - "a sparse deep factorization machine for efficient ctr prediction"
    - "feature generation by convolutional neural network for click through rate prediction"
    - "operation aware neural networks for user response prediction"
    - "xdeepfm combining explicit and implicit feature interactions for recommender systems"
    - "field weighted factorization machines for click through rate prediction in display advertising"
    - "pbodl parallel bayesian online deep learning for click through rate prediction in tencent advertising system"
    - "neural factorization machines for sparse predictive analytics"
    - "field aware factorization machines in a real world online advertising system"

Connected through hyperedge HE-8 (induced by source paper "adaptive factorization network learning adaptive order feature interactions"):
  with members:
    - "autoint automatic feature interaction learning via self attentive neural networks"
    - "xdeepfm combining explicit and implicit feature interactions for recommender systems"
    - "neural factorization machines for sparse predictive analytics"
    - "deep cross network for ad click predictions"
    - "deepfm a factorization machine based neural network for ctr prediction"
    - "attentional factorization machines learning the weight of feature interactions via attention networks"
    - "cross device user matching based on massive browse logs the runner up solution for the 2016 cikm cup"
    - "wide deep learning for recommender systems"

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)