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: "convolutional geometric matrix completion"
  Abstract: Geometric matrix completion~(GMC) has been proposed for recommendation by integrating the relationship~(link) graphs among users/items into matrix completion~(MC) . Traditional \mbox{GMC} methods typically adopt graph regularization to impose smoothness priors for \mbox{MC}. Recently, geometric deep learning on graphs~(\mbox{GDLG}) is proposed to solve the GMC problem, showing better performance than existing GMC methods including traditional graph regularization based methods. To the best of our knowledge, there exists only one GDLG method for GMC, which is called \mbox{RMGCNN}. RMGCNN combines graph convolutional network~(GCN) and recurrent neural network~(RNN) together for GMC. In the original work of RMGCNN, RMGCNN demonstrates better performance than pure GCN-based method. In this paper, we propose a new \mbox{GMC} method, called \underline{c}onvolutional \underline{g}eometric \underline{m}atrix \underline{c}ompletion~(CGMC), for recommendation with graphs among users/items. CGMC is a pure GCN-based method with a newly designed graph convolutional network. Experimental results on real datasets show that CGMC can outperform other state-of-the-art methods including RMGCNN.

Connected through hyperedge HE-1 (induced by source paper "self attention graph pooling"):
  with members:
    - "graph u nets"
    - "fast graph representation learning with pytorch geometric"
    - "graph convolutional policy network for goal directed molecular graph generation"
    - "graph neural networks a review of methods and applications"
    - "pitfalls of graph neural network evaluation"
    - "hierarchical graph representation learning with differentiable pooling"
    - "weisfeiler and leman go neural higher order graph neural networks"
    - "representation learning on graphs with jumping knowledge networks"

Connected through hyperedge HE-2 (induced by source paper "collaborative self attention for recommender systems"):
  with members:
    - "outer product based neural collaborative filtering"
    - "multi pointer co attention networks for recommendation"
    - "attention is all you need"
    - "neural collaborative filtering"
    - "deep semantic role labeling with self attention"
    - "global optimality in low rank matrix optimization"
    - "scalable robust matrix factorization with nonconvex loss"
    - "no spurious local minima in nonconvex low rank problems a unified geometric analysis"

Connected through hyperedge HE-3 (induced by source paper "scalable probabilistic matrix factorization with graph based priors"):
  with members:
    - "geometric matrix completion with recurrent multi graph neural networks"
    - "depth image inpainting improving low rank matrix completion with low gradient regularization"
    - "graph learning from data under laplacian and structural constraints"
    - "an overview of low rank matrix recovery from incomplete observations"
    - "graph summarization methods and applications a survey"
    - "learning laplacian matrix in smooth graph signal representations"
    - "matrix completion with noise"
    - "exact matrix completion via convex optimization"

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)