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: "logdet rank minimization with application to subspace clustering"
  Abstract: Low-rank matrix is desired in many machine learning and computer vision problems. Most of the recent studies use the nuclear norm as a convex surrogate of the rank operator. However, all singular values are simply added together by the nuclear norm, and thus the rank may not be well approximated in practical problems. In this paper, we propose using a log-determinant (LogDet) function as a smooth and closer, though nonconvex, approximation to rank for obtaining a low-rank representation in subspace clustering. Augmented Lagrange multipliers strategy is applied to iteratively optimize the LogDet-based nonconvex objective function on potentially large-scale data. By making use of the angular information of principal directions of the resultant low-rank representation, an affinity graph matrix is constructed for spectral clustering. Experimental results on motion segmentation and face clustering data demonstrate that the proposed method often outperforms state-of-the-art subspace clustering algorithms.

Connected through hyperedge HE-1 (induced by source paper "fast low rank bayesian matrix completion with hierarchical gaussian prior models"):
  with members:
    - "guaranteed matrix completion via non convex factorization"
    - "bilinear generalized approximate message passing part i derivation"
    - "enhancing sparsity and resolution via reweighted atomic norm minimization"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"
    - "generalized approximate message passing for estimation with random linear mixing"
    - "message passing algorithms for compressed sensing i motivation and construction"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-2 (induced by source paper "top n recommendation with novel rank approximation"):
  with members:
    - "top n recommender system via matrix completion"
    - "robust subspace clustering via tighter rank approximation"
    - "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-3 (induced by source paper "robust subspace clustering via tighter rank approximation"):
  with members:
    - "generalized nonconvex nonsmooth low rank minimization"
    - "robust recovery of subspace structures by low rank representation"
    - "efficient sparse group feature selection via nonconvex optimization"
    - "sparse subspace clustering algorithm theory and applications"
    - "a geometric analysis of subspace clustering with outliers"
    - "exact subspace segmentation and outlier detection by low rank representation"
    - "the power of convex relaxation near optimal matrix completion"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-4 (induced by source paper "robust subspace clustering via smoothed rank approximation"):
  with members:
    - "generalized nonconvex nonsmooth low rank minimization"
    - "robust recovery of subspace structures by low rank representation"
    - "sparse subspace clustering algorithm theory and applications"
    - "a geometric analysis of subspace clustering with outliers"
    - "exact subspace segmentation and outlier detection by low rank representation"
    - "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.CV(Computer Vision and Pattern Recognition)