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 tighter rank approximation"
  Abstract: Matrix rank minimization problem is in general NP-hard. The nuclear norm is used to substitute the rank function in many recent studies. Nevertheless, the nuclear norm approximation adds all singular values together and the approximation error may depend heavily on the magnitudes of singular values. This might restrict its capability in dealing with many practical problems. In this paper, an arctangent function is used as a tighter approximation to the rank function. We use it on the challenging subspace clustering problem. For this nonconvex minimization problem, we develop an effective optimization procedure based on a type of augmented Lagrange multipliers (ALM) method. Extensive experiments on face clustering and motion segmentation show that the proposed method is effective for rank approximation.

Connected through hyperedge HE-1 (induced by source paper "twin learning for similarity and clustering a unified kernel approach"):
  with members:
    - "robust subspace clustering via smoothed rank approximation"

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"
    - "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-3 (induced by source paper "top n recommender system via matrix completion"):
  with members:
    - "robust pca via nonconvex rank approximation"
    - "robust subspace clustering via smoothed 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-4 (induced by source paper "top n recommendation on graphs"):
  with members:
    - "top n recommendation with novel rank approximation"
    - "top n recommender system via matrix completion"
    - "bpr bayesian personalized ranking from implicit feedback"

Connected through hyperedge HE-5 (induced by source paper "robust pca via nonconvex rank approximation"):
  with members:
    - "robust subspace clustering via smoothed 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)