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: "matrix completion on graphs"
  Abstract: The problem of finding the missing values of a matrix given a few of its entries, called matrix completion, has gathered a lot of attention in the recent years. Although the problem under the standard low rank assumption is NP-hard, Cand\`es and Recht showed that it can be exactly relaxed if the number of observed entries is sufficiently large. In this work, we introduce a novel matrix completion model that makes use of proximity information about rows and columns by assuming they form communities. This assumption makes sense in several real-world problems like in recommender systems, where there are communities of people sharing preferences, while products form clusters that receive similar ratings. Our main goal is thus to find a low-rank solution that is structured by the proximities of rows and columns encoded by graphs. We borrow ideas from manifold learning to constrain our solution to be smooth on these graphs, in order to implicitly force row and column proximities. Our matrix recovery model is formulated as a convex non-smooth optimization problem, for which a well-posed iterative scheme is provided. We study and evaluate the proposed matrix completion on synthetic and real data, showing that the proposed structured low-rank recovery model outperforms the standard matrix completion model in many situations.

Connected through hyperedge HE-1 (induced by source paper "context aware visual compatibility prediction"):
  with members:
    - "graph convolutional neural networks for web scale recommender systems"
    - "learning type aware embeddings for fashion compatibility"
    - "relational inductive biases deep learning and graph networks"
    - "recommending outfits from personal closet"
    - "outfit generation and style extraction via bidirectional lstm and autoencoder"
    - "prototypical networks for few shot learning"
    - "creating capsule wardrobes from fashion images"
    - "geometric matrix completion with recurrent multi graph neural networks"

Connected through hyperedge HE-2 (induced by source paper "inductive matrix completion based on graph neural networks"):
  with members:
    - "fast graph representation learning with pytorch geometric"
    - "link prediction based on graph neural networks"
    - "graph convolutional neural networks for web scale recommender systems"
    - "hierarchical graph representation learning with differentiable pooling"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "how powerful are graph neural networks"
    - "spectral collaborative filtering"
    - "geometric matrix completion with recurrent multi graph neural networks"

Connected through hyperedge HE-3 (induced by source paper "geometric matrix completion with deep conditional random fields"):
  with members:
    - "regularizing autoencoder based matrix completion models via manifold learning"
    - "geometric matrix completion with recurrent multi graph neural networks"
    - "geometric deep learning going beyond euclidean data"
    - "deep learning markov random field for semantic segmentation"
    - "robust on line matrix completion on graphs"
    - "semi supervised classification with graph convolutional networks"
    - "a neural autoregressive approach to collaborative filtering"
    - "hybrid recommender system based on autoencoders"

Connected through hyperedge HE-4 (induced by source paper "laplacian regularized graph bandits algorithms and theoretical analysis"):
  with members:
    - "a smoothed analysis of the greedy algorithm for the linear contextual bandit problem"
    - "alternating linear bandits for online matrix factorization recommendation"
    - "almost optimal algorithms for linear stochastic bandits with heavy tailed payoffs"
    - "graph based recommendation system"
    - "horde of bandits using gaussian markov random fields"
    - "distributed clustering of linear bandits in peer to peer networks"
    - "collaborative filtering bandits"
    - "learning laplacian matrix in smooth graph signal representations"

Connected through hyperedge HE-5 (induced by source paper "multi component graph convolutional collaborative filtering"):
  with members:
    - "spectral collaborative filtering"
    - "semi supervised classification with graph convolutional networks"
    - "a neural autoregressive approach to collaborative filtering"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "deep convolutional networks on graph structured data"
    - "convolutional networks on graphs for learning molecular fingerprints"
    - "diffusion convolutional neural networks"
    - "integrating heterogeneous information via flexible regularization framework for recommendation"

Connected through hyperedge HE-6 (induced by source paper "deep geometric matrix completion are we doing it right"):
  with members:
    - "implicit regularization in deep matrix factorization"
    - "simplifying graph convolutional networks"
    - "zoomout spectral upsampling for efficient shape correspondence"
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "geometric matrix completion with recurrent multi graph neural networks"
    - "algorithmic regularization in over parameterized matrix recovery"
    - "neural network matrix factorization"
    - "functional correspondence by matrix completion"

Connected through hyperedge HE-7 (induced by source paper "error analysis on graph laplacian regularized estimator"):
  with members:
    - "alternating linear bandits for online matrix factorization recommendation"
    - "graph laplacian regularization for image denoising analysis in the continuous domain"
    - "network lasso clustering and optimization in large graphs"
    - "restricted strong convexity and weighted matrix completion optimal bounds with noise"
    - "consistency of trace norm minimization"

Connected through hyperedge HE-8 (induced by source paper "online matrix completion with side information"):
  with members:
    - "online similarity prediction of networked data from known and unknown graphs"
    - "near optimal algorithms for online matrix prediction"
    - "the power of convex relaxation near optimal matrix completion"
    - "exact matrix completion via convex optimization"
    - "low rank matrix factorization with attributes"

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)