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: "feature generation by convolutional neural network for click through rate prediction"
  Abstract: Click-Through Rate prediction is an important task in recommender systems, which aims to estimate the probability of a user to click on a given item. Recently, many deep models have been proposed to learn low-order and high-order feature interactions from original features. However, since useful interactions are always sparse, it is difficult for DNN to learn them effectively under a large number of parameters. In real scenarios, artificial features are able to improve the performance of deep models (such as Wide & Deep Learning), but feature engineering is expensive and requires domain knowledge, making it impractical in different scenarios. Therefore, it is necessary to augment feature space automatically. In this paper, We propose a novel Feature Generation by Convolutional Neural Network (FGCNN) model with two components: Feature Generation and Deep Classifier. Feature Generation leverages the strength of CNN to generate local patterns and recombine them to generate new features. Deep Classifier adopts the structure of IPNN to learn interactions from the augmented feature space. Experimental results on three large-scale datasets show that FGCNN significantly outperforms nine state-of-the-art models. Moreover, when applying some state-of-the-art models as Deep Classifier, better performance is always achieved, showing the great compatibility of our FGCNN model. This work explores a novel direction for CTR predictions: it is quite useful to reduce the learning difficulties of DNN by automatically identifying important features.

Connected through hyperedge HE-1 (induced by source paper "multi graph convolution collaborative filtering"):
  with members:
    - "graph convolutional neural networks for web scale recommender systems"
    - "product based neural networks for user response prediction over multi field categorical data"
    - "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 "deep personalized re targeting"):
  with members:
    - "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"
    - "word2vec applied to recommendation hyperparameters matter"

Connected through hyperedge HE-3 (induced by source paper "flen leveraging field for scalable ctr prediction"):
  with members:
    - "a sparse deep factorization machine for efficient ctr prediction"
    - "operation aware neural networks for user response prediction"
    - "product based neural networks for user response prediction over multi field categorical data"
    - "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"

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)