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: "greedy feature selection for subspace clustering"
  Abstract: Unions of subspaces provide a powerful generalization to linear subspace models for collections of high-dimensional data. To learn a union of subspaces from a collection of data, sets of signals in the collection that belong to the same subspace must be identified in order to obtain accurate estimates of the subspace structures present in the data. Recently, sparse recovery methods have been shown to provide a provable and robust strategy for exact feature selection (EFS)--recovering subsets of points from the ensemble that live in the same subspace. In parallel with recent studies of EFS with L1-minimization, in this paper, we develop sufficient conditions for EFS with a greedy method for sparse signal recovery known as orthogonal matching pursuit (OMP). Following our analysis, we provide an empirical study of feature selection strategies for signals living on unions of subspaces and characterize the gap between sparse recovery methods and nearest neighbor (NN)-based approaches. In particular, we demonstrate that sparse recovery methods provide significant advantages over NN methods and the gap between the two approaches is particularly pronounced when the sampling of subspaces in the dataset is sparse. Our results suggest that OMP may be employed to reliably recover exact feature sets in a number of regimes where NN approaches fail to reveal the subspace membership of points in the ensemble.

Connected through hyperedge HE-1 (induced by source paper "multi level representation learning for deep subspace clustering"):
  with members:
    - "self supervised convolutional subspace clustering network"
    - "deep spectral clustering using dual autoencoder network"
    - "neural collaborative subspace clustering"
    - "unsupervised image to image translation using domain specific variational information bound"
    - "clustergan latent space clustering in generative adversarial networks"
    - "deep multimodal subspace clustering networks"
    - "deep unsupervised clustering with gaussian mixture variational autoencoders"
    - "deep subspace clustering networks"

Connected through hyperedge HE-2 (induced by source paper "ell_0 motivated low rank sparse subspace clustering"):
  with members:
    - "bilinear factor matrix norm minimization for robust pca algorithms and applications"
    - "multi view low rank sparse subspace clustering"
    - "constructing the l2 graph for robust subspace learning and subspace clustering"
    - "enhanced low rank matrix approximation"
    - "improving compressed sensing with the diamond norm"
    - "nonconvex nonsmooth low rank minimization via iteratively reweighted nuclear norm"
    - "iterative concave rank approximation for recovering low rank matrices"
    - "generalized nonconvex nonsmooth low rank minimization"

Connected through hyperedge HE-3 (induced by source paper "simultaneous subspace clustering and cluster number estimating based on triplet relationship"):
  with members:
    - "structured sparse subspace clustering a joint affinity learning and subspace clustering framework"
    - "tensor robust principal component analysis exact recovery of corrupted low rank tensors via convex optimization"
    - "constructing the l2 graph for robust subspace learning and subspace clustering"
    - "oracle based active set algorithm for scalable elastic net subspace clustering"
    - "facenet a unified embedding for face recognition and clustering"
    - "correlation adaptive subspace segmentation by trace lasso"
    - "scalable sparse subspace clustering by orthogonal matching pursuit"
    - "robust and efficient subspace segmentation via least squares regression"

Connected through hyperedge HE-4 (induced by source paper "three stage subspace clustering framework with graph based transformation and optimization"):
  with members:
    - "subspace clustering by block diagonal representation"
    - "structured sparse subspace clustering a joint affinity learning and subspace clustering framework"
    - "oracle based active set algorithm for scalable elastic net subspace clustering"
    - "kernel sparse subspace clustering on symmetric positive definite manifolds"
    - "correntropy induced l2 graph for robust subspace clustering"
    - "scalable sparse subspace clustering by orthogonal matching pursuit"
    - "robust and efficient subspace segmentation via least squares regression"
    - "robust recovery of subspace structures by low rank representation"

Connected through hyperedge HE-5 (induced by source paper "basis pursuit and orthogonal matching pursuit for subspace preserving recovery theoretical analysis"):
  with members:
    - "noisy subspace clustering via matching pursuits"
    - "provable self representation based outlier detection in a union of subspaces"
    - "scalable sparse subspace clustering by orthogonal matching pursuit"
    - "robust subspace clustering"
    - "sparse modeling for image and vision processing"
    - "sparse representation of a polytope and recovery of sparse signals and low rank matrices"
    - "one condition for solution uniqueness and robustness of both l1 synthesis and l1 analysis minimizations"
    - "a remark on the restricted isometry property in orthogonal matching pursuit"

Connected through hyperedge HE-6 (induced by source paper "compressed subspace learning based on canonical angle preserving property"):
  with members:
    - "johnson lindenstrauss property implies subspace restricted isometry property"
    - "rigorous restricted isometry property of low dimensional subspaces"
    - "restricted isometry property of gaussian random projection for finite set of subspaces"
    - "what happens to a manifold under a bi lipschitz map"
    - "scalable sparse subspace clustering by orthogonal matching pursuit"
    - "dimensionality reduction with subgaussian matrices a unified theory"
    - "the restricted isometry property for random block diagonal matrices"
    - "sparse subspace clustering algorithm theory and applications"

Connected through hyperedge HE-7 (induced by source paper "theory of spectral method for union of subspaces based random geometry graph"):
  with members:
    - "active orthogonal matching pursuit for sparse subspace clustering"
    - "restricted isometry property of gaussian random projection for finite set of subspaces"
    - "stochastic block model and community detection in the sparse graphs a spectral algorithm with optimal rate of recovery"
    - "robust subspace clustering"
    - "robust recovery of subspace structures by low rank representation"
    - "generalized principal component analysis gpca"
    - "a geometric analysis of subspace clustering with outliers"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-8 (induced by source paper "a novel efficient approach with data adaptive capability for omp based sparse subspace clustering"):
  with members:
    - "kernel sparse subspace clustering on symmetric positive definite manifolds"
    - "scalable sparse subspace clustering by orthogonal matching pursuit"
    - "active dictionary learning in sparse representation based classification"
    - "sparse subspace clustering algorithm theory and applications"
    - "sparse recovery with orthogonal matching pursuit under rip"
    - "subspace pursuit for compressive sensing signal reconstruction"
    - "a tutorial on spectral clustering"

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.LG(Machine Learning)