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: "constructing the l2 graph for robust subspace learning and subspace clustering"
  Abstract: Under the framework of graph-based learning, the key to robust subspace clustering and subspace learning is to obtain a good similarity graph that eliminates the effects of errors and retains only connections between the data points from the same subspace (i.e., intrasubspace data points). Recent works achieve good performance by modeling errors into their objective functions to remove the errors from the inputs. However, these approaches face the limitations that the structure of errors should be known prior and a complex convex problem must be solved. In this paper, we present a novel method to eliminate the effects of the errors from the projection space (representation) rather than from the input space. We first prove that    $\boldsymbol {\ell }_{\boldsymbol {1}}$   -,    $\boldsymbol {\ell }_{\boldsymbol {2}}$   -,    $\boldsymbol {\ell }_{\boldsymbol {\infty }}$   -, and nuclear-norm-based linear projection spaces share the property of intrasubspace projection dominance, i.e., the coefficients over intrasubspace data points are larger than those over intersubspace data points. Based on this property, we introduce a method to construct a sparse similarity graph, called L2-graph. The subspace clustering and subspace learning algorithms are developed upon L2-graph. We conduct comprehensive experiment on subspace learning, image clustering, and motion segmentation and consider several quantitative benchmarks classification/clustering accuracy, normalized mutual information, and running time. Results show that L2-graph outperforms many state-of-the-art methods in our experiments, including L1-graph, low rank representation (LRR), and latent LRR, least square regression, sparse subspace clustering, and locally linear representation.

Connected through hyperedge HE-1 (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"
    - "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"
    - "generalized singular value thresholding"

Connected through hyperedge HE-2 (induced by source paper "structure learning with similarity preserving"):
  with members:
    - "multi view multiple clustering"
    - "similarity learning via kernel preserving embedding"
    - "deep k means jointly clustering with k means and learning representations"
    - "deep subspace clustering networks"
    - "twin learning for similarity and clustering a unified kernel approach"
    - "top n recommender system via matrix completion"
    - "unsupervised deep embedding for clustering analysis"
    - "scalable sparse subspace clustering by orthogonal matching pursuit"

Connected through hyperedge HE-3 (induced by source paper "self supervised convolutional subspace clustering network"):
  with members:
    - "constrained sparse subspace clustering with side information"
    - "structured sparse subspace clustering a joint affinity learning and subspace clustering framework"
    - "deep sparse subspace clustering"
    - "deep subspace clustering networks"
    - "non linear subspace clustering with learned low rank kernels"
    - "ol e orthogonal low rank embedding a plug and play geometric loss for deep learning"
    - "adaptive low rank kernel subspace clustering"
    - "oracle based active set algorithm for scalable elastic net subspace clustering"

Connected through hyperedge HE-4 (induced by source paper "robust subspace clustering by cauchy loss function"):
  with members:
    - "multi view intact space learning"
    - "sce a manifold regularized set covering method for data partitioning"
    - "a unified framework for representation based subspace clustering of out of sample and large scale data"
    - "joint unsupervised learning of deep representations and image clusters"
    - "correlation adaptive subspace segmentation by trace lasso"
    - "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 "clustering aware graph construction a joint learning perspective"):
  with members:
    - "learning graphs from data a signal representation perspective"
    - "joint label prediction based semi supervised adaptive concept factorization for robust data representation"
    - "structured sparse subspace clustering a joint affinity learning and subspace clustering framework"
    - "graph learning from data under laplacian and structural constraints"
    - "learning sparse graphs under smoothness prior"
    - "robust principal component analysis on graphs"
    - "learning laplacian matrix in smooth graph signal representations"
    - "robust and efficient subspace segmentation via least squares regression"

Connected through hyperedge HE-6 (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"
    - "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"
    - "robust recovery of subspace structures by low rank representation"

Connected through hyperedge HE-7 (induced by source paper "robust graph learning from noisy data"):
  with members:
    - "low rank kernel learning for graph based clustering"
    - "similarity learning via kernel preserving embedding"
    - "yotube searching action proposal via recurrent and static regression networks"
    - "multiview spectral clustering via structured low rank matrix factorization"
    - "connections between nuclear norm and frobenius norm based representations"
    - "robust principal component analysis on graphs"
    - "unsupervised feature selection with adaptive structure learning"
    - "robust pca via nonconvex rank approximation"

Connected through hyperedge HE-8 (induced by source paper "k meansnet when k means meets differentiable programming"):
  with members:
    - "theoretical linear convergence of unfolded ista and its practical weights and thresholds"
    - "demystifying differentiable programming shift reset the penultimate backpropagator"
    - "subspace clustering by block diagonal representation"
    - "network dissection quantifying interpretability of deep visual representations"
    - "attention is all you need"
    - "deep subspace clustering networks"
    - "interpretable convolutional neural networks"
    - "oracle based active set algorithm for scalable elastic net subspace 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.CV(Computer Vision and Pattern Recognition)