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: "convex sparse spectral clustering single view to multi view"
  Abstract: Spectral clustering (SC) is one of the most widely used methods for data clustering. It first finds a low-dimensional embedding U of data by computing the eigenvectors of the normalized Laplacian matrix, and then performs k-means on $ {\text {U}}^\top $ to get the final clustering result. In this paper, we observe that, in the ideal case, $ {\text {U}} {\text {U}} ^\top $ should be block diagonal and thus sparse. Therefore, we propose the sparse SC (SSC) method that extends the SC with sparse regularization on $ {\text {U}} {\text {U}} ^\top $ . To address the computational issue of the nonconvex SSC model, we propose a novel convex relaxation of SSC based on the convex hull of the fixed rank projection matrices. Then, the convex SSC model can be efficiently solved by the alternating direction method of multipliers Furthermore, we propose the pairwise SSC that extends SSC to boost the clustering performance by using the multi-view information of data. Experimental comparisons with several baselines on real-world datasets testify to the efficacy of our proposed methods.

Connected through hyperedge HE-1 (induced by source paper "multi view low rank sparse subspace clustering"):
  with members:
    - "multi view intact space learning"
    - "a survey on multi view learning"
    - "robust recovery of subspace structures by low rank representation"
    - "sparse subspace clustering algorithm theory and applications"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-2 (induced by source paper "structured sparse subspace clustering a joint affinity learning and subspace clustering framework"):
  with members:
    - "connections between nuclear norm and frobenius norm based representations"
    - "noisy subspace clustering via matching pursuits"
    - "constructing the l2 graph for robust subspace learning and subspace clustering"
    - "oracle based active set algorithm for scalable elastic net subspace clustering"
    - "sssc am a unified framework for video co segmentation by structured sparse subspace clustering with appearance and motion features"
    - "correntropy induced l2 graph for robust subspace clustering"
    - "correlation adaptive subspace segmentation by trace lasso"
    - "scalable sparse subspace clustering by orthogonal matching pursuit"

Connected through hyperedge HE-3 (induced by source paper "nonconvex sparse spectral clustering by alternating direction method of multipliers and its convergence analysis"):
  with members:
    - "nonconvex nonsmooth low rank minimization via iteratively reweighted nuclear norm"
    - "generalized singular value thresholding"
    - "linearized alternating direction method with parallel splitting and adaptive penalty for separable convex programs in machine learning"
    - "sparse subspace clustering algorithm theory and applications"

Connected through hyperedge HE-4 (induced by source paper "robust localized multi view subspace clustering"):
  with members:
    - "a survey on multi view learning"
    - "robust recovery of subspace structures by low rank representation"
    - "sparse subspace clustering algorithm theory and applications"

Connected through hyperedge HE-5 (induced by source paper "on unifying multi view self representations for clustering by tensor multi rank minimization"):
  with members:
    - "tensor robust principal component analysis exact recovery of corrupted low rank tensors via convex optimization"
    - "unsupervised high level feature learning by ensemble projection for semi supervised image classification and image clustering"
    - "very deep convolutional networks for large scale image recognition"
    - "novel methods for multilinear data completion and de noising based on tensor svd"
    - "tensor based formulation and nuclear norm regularization for multienergy computed tomography"
    - "a survey on multi view learning"
    - "robust recovery of subspace structures by low rank representation"
    - "sparse subspace clustering algorithm theory and applications"

Connected through hyperedge HE-6 (induced by source paper "a unified alternating direction method of multipliers by majorization minimization"):
  with members:
    - "correlation adaptive subspace segmentation by trace lasso"
    - "linearized alternating direction method with parallel splitting and adaptive penalty for separable convex programs in machine learning"
    - "robust recovery of subspace structures by low rank representation"
    - "simultaneously structured models with application to sparse and low rank matrices"
    - "sparse subspace clustering algorithm theory and applications"

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)