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: "top n recommender system via matrix completion"
  Abstract: Top-N recommender systems have been investigated widely both in industry and academia. However, the recommendation quality is far from satisfactory. In this paper, we propose a simple yet promising algorithm. We fill the user-item matrix based on a low-rank assumption and simultaneously keep the original information. To do that, a nonconvex rank relaxation rather than the nuclear norm is adopted to provide a better rank approximation and an efficient optimization strategy is designed. A comprehensive set of experiments on real datasets demonstrates that our method pushes the accuracy of Top-N recommendation to a new level.

Connected through hyperedge HE-1 (induced by source paper "structure learning with similarity preserving"):
  with members:
    - "multi view multiple clustering"
    - "similarity learning via kernel preserving embedding"
    - "deep k means jointly clustering with k means and learning representations"
    - "deep subspace clustering networks"
    - "twin learning for similarity and clustering a unified kernel approach"
    - "constructing the l2 graph for robust subspace learning and subspace clustering"
    - "unsupervised deep embedding for clustering analysis"
    - "scalable sparse subspace clustering by orthogonal matching pursuit"

Connected through hyperedge HE-2 (induced by source paper "leveraging high dimensional side information for top n recommendation"):
  with members:
    - "iterative views agreement an iterative low rank based structured optimization method to multi view spectral clustering"
    - "vbpr visual bayesian personalized ranking from implicit feedback"
    - "collaborative deep learning for recommender systems"

Connected through hyperedge HE-3 (induced by source paper "top n recommendation with novel rank approximation"):
  with members:
    - "robust subspace clustering via tighter rank approximation"
    - "logdet rank minimization with application to subspace clustering"
    - "robust pca via nonconvex rank approximation"
    - "robust subspace clustering via smoothed rank approximation"
    - "generalized nonconvex nonsmooth low rank minimization"
    - "generalized singular value thresholding"
    - "bpr bayesian personalized ranking from implicit feedback"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-4 (induced by source paper "top n recommendation on graphs"):
  with members:
    - "top n recommendation with novel rank approximation"
    - "robust subspace clustering via tighter rank approximation"
    - "bpr bayesian personalized ranking from implicit feedback"

Connected through hyperedge HE-5 (induced by source paper "implicit feedback recommendation using method of moment"):
  with members:
    - "a stochastic pca and svd algorithm with an exponential convergence rate"
    - "spectral experts for estimating mixtures of linear regressions"
    - "a practical algorithm for topic modeling with provable guarantees"
    - "bpr bayesian personalized ranking from implicit feedback"
    - "tensor decompositions for learning latent variable models"
    - "learning mixtures of spherical gaussians moment methods and spectral decompositions"
    - "a spectral algorithm for latent dirichlet allocation"
    - "a spectral algorithm for learning hidden markov models"

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.IR(Information Retrieval)