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: "geometric matrix completion with recurrent multi graph neural networks"
  Abstract: Matrix completion models are among the most common formulations of recommender systems. Recent works have showed a boost of performance of these techniques when introducing the pairwise relationships between users/items in the form of graphs, and imposing smoothness priors on these graphs. However, such techniques do not fully exploit the local stationarity structures of user/item graphs, and the number of parameters to learn is linear w.r.t. the number of users and items. We propose a novel approach to overcome these limitations by using geometric deep learning on graphs. Our matrix completion architecture combines graph convolutional neural networks and recurrent neural networks to learn meaningful statistical graph-structured patterns and the non-linear diffusion process that generates the known ratings. This neural network system requires a constant number of parameters independent of the matrix size. We apply our method on both synthetic and real datasets, showing that it outperforms state-of-the-art techniques.

Connected through hyperedge HE-1 (induced by source paper "advances in collaborative filtering and ranking"):
  with members:
    - "temporal collaborative ranking via personalized transformer"
    - "graph dna deep neighborhood aware graph encoding for collaborative filtering"
    - "stochastic shared embeddings data driven regularization of embedding layers"
    - "hierarchical gating networks for sequential recommendation"
    - "graph convolutional neural networks for web scale recommender systems"
    - "fine tuned language models for text classification"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a call for clarity in reporting bleu scores"

Connected through hyperedge HE-2 (induced by source paper "beyond clicks modeling multi relational item graph for session based target behavior prediction"):
  with members:
    - "session based recommendation with graph neural network"
    - "bert4rec sequential recommendation with bidirectional encoder representations from transformer"
    - "hierarchical temporal convolutional networks for dynamic recommender systems"
    - "repeatnet a repeat aware neural recommendation machine for session based recommendation"
    - "attention is all you need"
    - "personalizing session based recommendations with hierarchical recurrent neural networks"
    - "improved recurrent neural networks for session based recommendations"
    - "dual attention networks for multimodal reasoning and matching"

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"
    - "xdeepfm combining explicit and implicit feature interactions for recommender systems"
    - "deepinf social influence prediction with deep learning"

Connected through hyperedge HE-4 (induced by source paper "sketchgcn semantic sketch segmentation with graph convolutional networks"):
  with members:
    - "a comprehensive survey on graph neural networks"
    - "deepgcns can gcns go as deep as cnns"
    - "graph convolutional neural networks for web scale recommender systems"
    - "learning to sketch with shortcut cycle consistency"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "dynamic graph cnn for learning on point clouds"
    - "fast sketch segmentation and labeling with deep learning"
    - "dynamic edge conditioned filters in convolutional neural networks on graphs"

Connected through hyperedge HE-5 (induced by source paper "revisiting graph based collaborative filtering a linear residual graph convolutional network approach"):
  with members:
    - "a neural influence diffusion model for social recommendation"
    - "t gcn a temporal graph convolutional network for traffic prediction"
    - "simplifying graph convolutional networks"
    - "graph convolutional neural networks for web scale recommender systems"
    - "predict then propagate graph neural networks meet personalized pagerank"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "spectral collaborative filtering"
    - "neural collaborative filtering"

Connected through hyperedge HE-6 (induced by source paper "dynamic graph convolutional networks"):
  with members:
    - "automated pruning for deep neural network compression"
    - "temporal dynamic graph lstm for action driven video object detection"
    - "gated graph sequence neural networks"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "convolutional networks on graphs for learning molecular fingerprints"
    - "structural rnn deep learning on spatio temporal graphs"

Connected through hyperedge HE-7 (induced by source paper "a gentle introduction to deep learning for graphs"):
  with members:
    - "deep tree transductions a short survey"
    - "gmnn graph markov neural networks"
    - "a model to search for synthesizable molecules"
    - "graph u nets"
    - "a fair comparison of graph neural networks for graph classification"
    - "self attention graph pooling"
    - "deep graph library towards efficient and scalable deep learning on graphs"
    - "fast and deep graph neural networks"

Connected through hyperedge HE-8 (induced by source paper "deepgcns making gcns go as deep as cnns"):
  with members:
    - "cluster gcn an efficient algorithm for training deep and large graph convolutional networks"
    - "fast graph representation learning with pytorch geometric"
    - "a comprehensive survey on graph neural networks"
    - "deepgcns can gcns go as deep as cnns"
    - "graph neural networks a review of methods and applications"
    - "partnet a large scale benchmark for fine grained and hierarchical part level 3d object understanding"
    - "graph convolutional neural networks for web scale recommender systems"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"

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)