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: "robust subspace clustering via smoothed rank approximation"
  Abstract: Matrix rank minimizing subject to affine constraints arises in many application areas, ranging from signal processing to machine learning. Nuclear norm is a convex relaxation for this problem which can recover the rank exactly under some restricted and theoretically interesting conditions. However, for many real-world applications, nuclear norm approximation to the rank function can only produce a result far from the optimum. To seek a solution of higher accuracy than the nuclear norm, in this letter, we propose a rank approximation based on Logarithm-Determinant. We consider using this rank approximation for subspace clustering application. Our framework can model different kinds of errors and noise. Effective optimization strategy is developed with theoretical guarantee to converge to a stationary point. The proposed method gives promising results on face clustering and motion segmentation tasks compared to the state-of-the-art subspace clustering algorithms.

Connected through hyperedge HE-1 (induced by source paper "minimal sample subspace learning theory and algorithms"):
  with members:
    - "subspace clustering by block diagonal representation"
    - "structured sparse subspace clustering a joint affinity learning and subspace clustering framework"
    - "innovation pursuit a new approach to subspace clustering"
    - "robust recovery of subspace structures by low rank representation"
    - "generalized principal component analysis gpca"
    - "sparse subspace clustering algorithm theory and applications"
    - "a geometric analysis of subspace clustering with outliers"
    - "comparing measures of sparsity"

Connected through hyperedge HE-2 (induced by source paper "large scale multi view subspace clustering in linear time"):
  with members:
    - "robust graph learning from noisy data"
    - "multi view multiple clustering"
    - "spectral clustering via ensemble deep autoencoder learning sc edae"
    - "multiple partitions aligned clustering"
    - "clustering with similarity preserving"
    - "efficient solvers for sparse subspace clustering"
    - "multi view low rank sparse subspace clustering"
    - "iterative views agreement an iterative low rank based structured optimization method to multi view spectral clustering"

Connected through hyperedge HE-3 (induced by source paper "low rank kernel learning for graph based clustering"):
  with members:
    - "yotube searching action proposal via recurrent and static regression networks"
    - "robust pca via nonconvex rank approximation"
    - "robust principal component analysis"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-4 (induced by source paper "discriminative transformation learning for fuzzy sparse subspace clustering"):
  with members:
    - "constructing the l2 graph for robust subspace learning and subspace clustering"
    - "discriminative nonlinear analysis operator learning when cosparse model meets image classification"
    - "joint sparse recovery with semisupervised music"
    - "oracle based active set algorithm for scalable elastic net subspace clustering"
    - "compressive spectral clustering"
    - "robust subspace clustering"
    - "robust and efficient subspace segmentation via least squares regression"
    - "learning transformations for clustering and classification"

Connected through hyperedge HE-5 (induced by source paper "twin learning for similarity and clustering a unified kernel approach"):
  with members:
    - "robust subspace clustering via tighter rank approximation"

Connected through hyperedge HE-6 (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"
    - "logdet rank minimization with application to subspace clustering"
    - "robust pca via nonconvex 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-7 (induced by source paper "top n recommender system via matrix completion"):
  with members:
    - "robust subspace clustering via tighter rank approximation"
    - "robust pca via nonconvex rank approximation"
    - "generalized nonconvex nonsmooth low rank minimization"
    - "empirical analysis of predictive algorithms for collaborative filtering"
    - "bpr bayesian personalized ranking from implicit feedback"
    - "collaborative filtering in a non uniform world learning with the weighted trace norm"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-8 (induced by source paper "robust pca via nonconvex rank approximation"):
  with members:
    - "robust subspace clustering via tighter rank approximation"
    - "non convex robust pca"
    - "robust recovery of subspace structures by low rank representation"
    - "efficient sparse group feature selection via nonconvex optimization"
    - "robust pca via outlier pursuit"
    - "robust principal component analysis"

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)