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: "fixed rank representation for unsupervised visual learning"
  Abstract: Subspace clustering and feature extraction are two of the most commonly used unsupervised learning techniques in computer vision and pattern recognition. State-of-the-art techniques for subspace clustering make use of recent advances in sparsity and rank minimization. However, existing techniques are computationally expensive and may result in degenerate solutions that degrade clustering performance in the case of insufficient data sampling. To partially solve these problems, and inspired by existing work on matrix factorization, this paper proposes fixed-rank representation (FRR) as a unified framework for unsupervised visual learning. FRR is able to reveal the structure of multiple subspaces in closed-form when the data is noiseless. Furthermore, we prove that under some suitable conditions, even with insufficient observations, FRR can still reveal the true subspace memberships. To achieve robustness to outliers and noise, a sparse regularizer is introduced into the FRR framework. Beyond subspace clustering, FRR can be used for unsupervised feature extraction. As a non-trivial byproduct, a fast numerical solver is developed for FRR. Experimental results on both synthetic data and real applications validate our theoretical analysis and demonstrate the benefits of FRR for unsupervised visual learning.

Connected through hyperedge HE-1 (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"
    - "constructing the l2 graph for robust subspace learning and subspace clustering"

Connected through hyperedge HE-2 (induced by source paper "robust multi subspace analysis using novel column l0 norm constrained matrix factorization"):
  with members:
    - "robust recovery of subspace structures by low rank representation"
    - "generalized principal component analysis gpca"
    - "sparse subspace clustering algorithm theory and applications"
    - "robust pca via outlier pursuit"
    - "matrix completion with noise"
    - "robust principal component analysis"
    - "exact matrix completion via convex optimization"
    - "on the complexity of nonnegative matrix factorization"

Connected through hyperedge HE-3 (induced by source paper "deep sparse subspace clustering"):
  with members:
    - "deep subspace clustering networks"
    - "constructing the l2 graph for robust subspace learning and subspace clustering"
    - "joint unsupervised learning of deep representations and image clusters"
    - "facenet a unified embedding for face recognition and clustering"
    - "unsupervised deep embedding for clustering analysis"
    - "scalable sparse subspace clustering by orthogonal matching pursuit"
    - "robust subspace clustering"
    - "a new approach to two view motion segmentation using global dimension minimization"

Connected through hyperedge HE-4 (induced by source paper "efficient sparse subspace clustering by nearest neighbour filtering"):
  with members:
    - "constructing the l2 graph for robust subspace learning and subspace clustering"
    - "oracle based active set algorithm for scalable elastic net subspace clustering"
    - "scalable sparse subspace clustering by orthogonal matching pursuit"
    - "robust subspace clustering"
    - "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"
    - "greedy feature selection for subspace clustering"
    - "generalized principal component analysis gpca"

Connected through hyperedge HE-5 (induced by source paper "constructing the l2 graph for robust subspace learning and subspace clustering"):
  with members:
    - "robust subspace clustering"
    - "learning locality constrained collaborative representation for robust face recognition"
    - "robust and efficient subspace segmentation via least squares regression"
    - "low rank structure learning via nonconvex heuristic recovery"
    - "robust recovery of subspace structures by low rank representation"
    - "generalized principal component analysis gpca"
    - "sparse subspace clustering algorithm theory and applications"
    - "matrix completion with noise"

Connected through hyperedge HE-6 (induced by source paper "automatic subspace learning via principal coefficients embedding"):
  with members:
    - "multi view intact space learning"
    - "connections between nuclear norm and frobenius norm based representations"
    - "constructing the l2 graph for robust subspace learning and subspace clustering"
    - "robust recovery of subspace structures by low rank representation"
    - "sparse subspace clustering algorithm theory and applications"
    - "robust principal component analysis"
    - "non negative matrix factorization with sparseness constraints"

Connected through hyperedge HE-7 (induced by source paper "collaborative low rank subspace clustering"):
  with members:
    - "robust subspace clustering"
    - "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"
    - "sparse subspace clustering algorithm theory and applications"
    - "building high level features using large scale unsupervised learning"
    - "analysis and improvement of low rank representation for subspace segmentation"
    - "robust principal component analysis"

Connected through hyperedge HE-8 (induced by source paper "constrained low rank learning using least squares based regularization"):
  with members:
    - "fast low rank representation based spatial pyramid matching for image classification"
    - "robust recovery of subspace structures by low rank representation"
    - "relational multimanifold coclustering"
    - "sparse subspace clustering algorithm theory and applications"
    - "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)