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: "provable inductive matrix completion"
  Abstract: Consider a movie recommendation system where apart from the ratings information, side information such as user's age or movie's genre is also available. Unlike standard matrix completion, in this setting one should be able to predict inductively on new users/movies. In this paper, we study the problem of inductive matrix completion in the exact recovery setting. That is, we assume that the ratings matrix is generated by applying feature vectors to a low-rank matrix and the goal is to recover back the underlying matrix. Furthermore, we generalize the problem to that of low-rank matrix estimation using rank-1 measurements. We study this generic problem and provide conditions that the set of measurements should satisfy so that the alternating minimization method (which otherwise is a non-convex method with no convergence guarantees) is able to recover back the {\em exact} underlying low-rank matrix. #R##N#In addition to inductive matrix completion, we show that two other low-rank estimation problems can be studied in our framework: a) general low-rank matrix sensing using rank-1 measurements, and b) multi-label regression with missing labels. For both the problems, we provide novel and interesting bounds on the number of measurements required by alternating minimization to provably converges to the {\em exact} low-rank matrix. In particular, our analysis for the general low rank matrix sensing problem significantly improves the required storage and computational cost than that required by the RIP-based matrix sensing methods \cite{RechtFP2007}. Finally, we provide empirical validation of our approach and demonstrate that alternating minimization is able to recover the true matrix for the above mentioned problems using a small number of measurements.

Connected through hyperedge HE-1 (induced by source paper "matrix completion with prior subspace information via maximizing correlation"):
  with members:
    - "optimal weighted low rank matrix recovery with subspace prior information"
    - "recovery of structured signals with prior information via maximizing correlation"
    - "weighted matrix completion and recovery with prior subspace information"
    - "incoherence optimal matrix completion"
    - "recovering low rank matrices from few coefficients in any basis"
    - "clustering partially observed graphs via convex optimization"
    - "learning with the weighted trace norm under arbitrary sampling distributions"
    - "restricted strong convexity and weighted matrix completion optimal bounds with noise"

Connected through hyperedge HE-2 (induced by source paper "rich item recommendations for rich users via gcnn exploiting dynamic and static side information"):
  with members:
    - "geometric deep learning on graphs and manifolds using mixture model cnns"
    - "semi supervised classification with graph convolutional networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "support vector algorithms for optimizing the partial area under the roc curve"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"
    - "distributed representations of words and phrases and their compositionality"
    - "direct optimization of ranking measures"

Connected through hyperedge HE-3 (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-4 (induced by source paper "collaborative self attention for recommender systems"):
  with members:
    - "convolutional geometric matrix completion"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "fast exact matrix completion a unifying optimization framework"):
  with members:
    - "don t decay the learning rate increase the batch size"
    - "provable efficient online matrix completion via non convex stochastic gradient descent"
    - "fast exact matrix completion with finite samples"
    - "clustering partially observed graphs via convex optimization"
    - "restricted strong convexity and weighted matrix completion optimal bounds with noise"
    - "the power of convex relaxation near optimal matrix completion"
    - "guaranteed rank minimization via singular value projection"
    - "matrix completion from a few entries"

Connected through hyperedge HE-6 (induced by source paper "a greedy algorithm for matrix recovery with subspace prior information"):
  with members:
    - "weighted matrix completion and recovery with prior subspace information"
    - "orthogonal rank one matrix pursuit for low rank matrix completion"

Connected through hyperedge HE-7 (induced by source paper "convolutional geometric matrix completion"):
  with members:
    - "nonconvex optimization meets low rank matrix factorization an overview"
    - "mixhop higher order graph convolutional architectures via sparsified neighborhood mixing"
    - "graph convolutional neural networks for web scale recommender systems"
    - "predict then propagate graph neural networks meet personalized pagerank"
    - "large scale learnable graph convolutional networks"
    - "adaptive sampling towards fast graph representation learning"
    - "geometric matrix completion with recurrent multi graph neural networks"
    - "neural message passing for quantum chemistry"

Connected through hyperedge HE-8 (induced by source paper "dual primal graph convolutional networks"):
  with members:
    - "modeling polypharmacy side effects with graph convolutional networks"
    - "dynamic graph cnn for learning on point clouds"
    - "motifnet a motif based graph convolutional network for directed graphs"
    - "geometric matrix completion with recurrent multi graph neural networks"
    - "neural message passing for quantum chemistry"
    - "cayleynets graph convolutional neural networks with complex rational spectral filters"
    - "higher order organization of complex networks"
    - "gated graph sequence neural networks"

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)