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: "orthogonal rank one matrix pursuit for low rank matrix completion"
  Abstract: In this paper, we propose an efficient and scalable low rank matrix completion algorithm. The key idea is to extend orthogonal matching pursuit method from the vector case to the matrix case. We further propose an economic version of our algorithm by introducing a novel weight updating rule to reduce the time and storage complexity. Both versions are computationally inexpensive for each matrix pursuit iteration, and find satisfactory results in a few iterations. Another advantage of our proposed algorithm is that it has only one tunable parameter, which is the rank. It is easy to understand and to use by the user. This becomes especially important in large-scale learning problems. In addition, we rigorously show that both versions achieve a linear convergence rate, which is significantly better than the previous known results. We also empirically compare the proposed algorithms with several state-of-the-art matrix completion algorithms on many real-world datasets, including the large-scale recommendation dataset Netflix as well as the MovieLens datasets. Numerical results show that our proposed algorithm is more efficient than competing algorithms while achieving similar or better prediction performance.

Connected through hyperedge HE-1 (induced by source paper "binary matrix completion with nonconvex regularizers"):
  with members:
    - "link prediction via matrix completion"
    - "positive unlabeled learning with non negative risk estimator"
    - "large scale low rank matrix learning with nonconvex regularizers"
    - "geometric matrix completion with recurrent multi graph neural networks"
    - "matrix completion via max norm constrained optimization"
    - "semi supervised classification based on classification from positive and unlabeled data"
    - "network topology inference from spectral templates"
    - "nonconvex nonsmooth low rank minimization via iteratively reweighted nuclear norm"

Connected through hyperedge HE-2 (induced by source paper "searching for interaction functions in collaborative filtering"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "efficient neural architecture search via proximal iterations"
    - "are we really making much progress a worrying analysis of recent neural recommendation approaches"
    - "taking human out of learning applications a survey on automated machine learning"
    - "darts differentiable architecture search"
    - "snas stochastic neural architecture search"
    - "learning transferable architectures for scalable image recognition"
    - "accelerated and inexact soft impute for large scale matrix and tensor completion"

Connected through hyperedge HE-3 (induced by source paper "efficient neural interaction function search for collaborative filtering"):
  with members:
    - "efficient neural architecture search via proximal iterations"
    - "autokge searching scoring functions for knowledge graph embedding"
    - "are we really making much progress a worrying analysis of recent neural recommendation approaches"
    - "taking human out of learning applications a survey on automated machine learning"
    - "darts differentiable architecture search"
    - "snas stochastic neural architecture search"
    - "learning transferable architectures for scalable image recognition"
    - "accelerated and inexact soft impute for large scale matrix and tensor completion"

Connected through hyperedge HE-4 (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"
    - "provable inductive matrix completion"

Connected through hyperedge HE-5 (induced by source paper "are object detection assessment criteria ready for maritime computer vision"):
  with members:
    - "long term temporal convolutions for action recognition"
    - "a deep matrix factorization method for learning attribute representations"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "object detection networks on convolutional feature maps"
    - "robust pca via nonconvex rank approximation"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-6 (induced by source paper "large scale low rank matrix learning with nonconvex regularizers"):
  with members:
    - "nonconvex nonsmooth low rank minimization via iteratively reweighted nuclear norm"
    - "partial sum minimization of singular values in robust pca algorithm and applications"
    - "fast randomized singular value thresholding for nuclear norm minimization"
    - "fast low rank matrix learning with nonconvex regularization"
    - "generalized singular value thresholding"
    - "robust recovery of subspace structures by low rank representation"
    - "a general iterative shrinkage and thresholding algorithm for non convex regularized optimization problems"
    - "convergence rates of inexact proximal gradient methods for convex optimization"

Connected through hyperedge HE-7 (induced by source paper "accelerated and inexact soft impute for large scale matrix and tensor completion"):
  with members:
    - "nonconvex nonsmooth low rank minimization via iteratively reweighted nuclear norm"
    - "parallel matrix factorization for low rank tensor completion"
    - "convergence rates of inexact proximal gradient methods for convex optimization"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-8 (induced by source paper "localization in the internet of things network a low rank matrix completion approach"):
  with members:
    - "fixed rank matrix factorizations and riemannian low rank optimization"
    - "the power of convex relaxation near optimal matrix completion"
    - "a simpler approach to matrix completion"

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)