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: "xdeepfm combining explicit and implicit feature interactions for recommender systems"
  Abstract: Combinatorial features are essential for the success of many commercial models. Manually crafting these features usually comes with high cost due to the variety, volume and velocity of raw data in web-scale systems. Factorization based models, which measure interactions in terms of vector product, can learn patterns of combinatorial features automatically and generalize to unseen features as well. With the great success of deep neural networks (DNNs) in various fields, recently researchers have proposed several DNN-based factorization model to learn both low- and high-order feature interactions. Despite the powerful ability of learning an arbitrary function from data, plain DNNs generate feature interactions implicitly and at the bit-wise level. In this paper, we propose a novel Compressed Interaction Network (CIN), which aims to generate feature interactions in an explicit fashion and at the vector-wise level. We show that the CIN share some functionalities with convolutional neural networks (CNNs) and recurrent neural networks (RNNs). We further combine a CIN and a classical DNN into one unified model, and named this new model eXtreme Deep Factorization Machine (xDeepFM). On one hand, the xDeepFM is able to learn certain bounded-degree feature interactions explicitly; on the other hand, it can learn arbitrary low- and high-order feature interactions implicitly. We conduct comprehensive experiments on three real-world datasets. Our results demonstrate that xDeepFM outperforms state-of-the-art models. We have released the source code of xDeepFM at https://github.com/Leavingseason/xDeepFM.

Connected through hyperedge HE-1 (induced by source paper "cross gcn enhancing graph convolutional network with k order feature interactions"):
  with members:
    - "mixhop higher order graph convolutional architectures via sparsified neighborhood mixing"
    - "graph representation learning via hard and channel wise attention networks"
    - "towards better text understanding and retrieval through kernel entity salience modeling"
    - "graph convolutional neural networks for web scale recommender systems"
    - "hypergraph neural networks"
    - "graph convolutional networks for text classification"
    - "deepinf social influence prediction with deep learning"
    - "learning structural node embeddings via diffusion wavelets"

Connected through hyperedge HE-2 (induced by source paper "a sparse deep factorization machine for efficient ctr prediction"):
  with members:
    - "autoint automatic feature interaction learning via self attentive neural networks"
    - "importance estimation for neural network pruning"
    - "structured pruning of neural networks with budget aware regularization"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "field weighted factorization machines for click through rate prediction in display advertising"
    - "rethinking the value of network pruning"
    - "neural factorization machines for sparse predictive analytics"
    - "deep cross network for ad click predictions"

Connected through hyperedge HE-3 (induced by source paper "diffnet a neural influence and interest diffusion network for social recommendation"):
  with members:
    - "a neural influence diffusion model for social recommendation"
    - "dual graph attention networks for deep latent representation of multifaceted social effects in recommender systems"
    - "star gcn stacked and reconstructed graph convolutional networks for recommender systems"
    - "graph neural networks for social recommendation"
    - "neural graph collaborative filtering"
    - "graph convolutional neural networks for web scale recommender systems"
    - "deepinf social influence prediction with deep learning"
    - "how powerful are graph neural networks"

Connected through hyperedge HE-4 (induced by source paper "price aware recommendation with graph convolutional networks"):
  with members:
    - "simplifying graph convolutional networks"
    - "mmalfm explainable recommendation by leveraging reviews and images"
    - "neural graph collaborative filtering"
    - "graph convolutional neural networks for web scale recommender systems"
    - "how powerful are graph neural networks"
    - "neural factorization machines for sparse predictive analytics"
    - "neural collaborative filtering"
    - "item silk road recommending items from information domains to social users"

Connected through hyperedge HE-5 (induced by source paper "privileged features distillation at taobao recommendations"):
  with members:
    - "ranking distillation learning compact ranking models with high performance for recommender system"
    - "deep learning under privileged information using heteroscedastic dropout"
    - "large scale distributed neural network training through online distillation"
    - "self attentive sequential recommendation"
    - "deep mutual learning"
    - "attention is all you need"
    - "apprentice using knowledge distillation techniques to improve low precision network accuracy"
    - "deepfm a factorization machine based neural network for ctr prediction"

Connected through hyperedge HE-6 (induced by source paper "adversarial multimodal representation learning for click through rate prediction"):
  with members:
    - "transfer learning with dynamic adversarial adaptation network"
    - "neural news recommendation with attentive multi view learning"
    - "multimodal machine learning a survey and taxonomy"
    - "improved recurrent neural networks for session based recommendations"
    - "unsupervised pixel level domain adaptation with generative adversarial networks"
    - "image based recommendations on styles and substitutes"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-7 (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"
    - "product based neural networks for user response prediction over multi field categorical data"
    - "a graph cnn for 3d point cloud classification"
    - "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-8 (induced by source paper "graph convolution machine for context aware recommender system"):
  with members:
    - "fi gnn modeling feature interactions via graph neural networks for ctr prediction"
    - "attentional factorization machines learning the weight of feature interactions via attention networks"
    - "wide deep learning for recommender systems"
    - "semi supervised classification with graph convolutional networks"
    - "adam a method for stochastic optimization"
    - "bpr bayesian personalized ranking from implicit feedback"

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)