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: "discriminative block diagonal representation learning for image recognition"
  Abstract: Existing block-diagonal representation studies mainly focuses on casting block-diagonal regularization on training data, while only little attention is dedicated to concurrently learning both block-diagonal representations of training and test data. In this paper, we propose a discriminative block-diagonal low-rank representation (BDLRR) method for recognition. In particular, the elaborate BDLRR is formulated as a joint optimization problem of shrinking the unfavorable representation from off-block-diagonal elements and strengthening the compact block-diagonal representation under the semisupervised framework of LRR. To this end, we first impose penalty constraints on the negative representation to eliminate the correlation between different classes such that the incoherence criterion of the extra-class representation is boosted. Moreover, a constructed subspace model is developed to enhance the self-expressive power of training samples and further build the representation bridge between the training and test samples, such that the coherence of the learned intraclass representation is consistently heightened. Finally, the resulting optimization problem is solved elegantly by employing an alternative optimization strategy, and a simple recognition algorithm on the learned representation is utilized for final prediction. Extensive experimental results demonstrate that the proposed method achieves superb recognition results on four face image data sets, three character data sets, and the 15 scene multicategories data set. It not only shows superior potential on image recognition but also outperforms the state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "feature concatenation multi view subspace clustering"):
  with members:
    - "multi view low rank sparse subspace clustering"
    - "a survey on multi view clustering"
    - "visual understanding via multi feature shared learning with global consistency"
    - "iterative views agreement an iterative low rank based structured optimization method to multi view spectral clustering"
    - "a survey on multi view learning"
    - "robust recovery of subspace structures by low rank representation"
    - "sparse subspace clustering algorithm theory and applications"
    - "analysis and improvement of low rank representation for subspace segmentation"

Connected through hyperedge HE-2 (induced by source paper "constrained bilinear factorization multi view subspace clustering"):
  with members:
    - "multi view low rank sparse subspace clustering"
    - "a survey on multi view clustering"
    - "a survey on multi view learning"
    - "robust recovery of subspace structures by low rank representation"
    - "sparse subspace clustering algorithm theory and applications"
    - "robust principal component analysis"
    - "exact matrix completion via convex optimization"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-3 (induced by source paper "learning a representation with the block diagonal structure for pattern classification"):
  with members:
    - "robust recovery of subspace structures by low rank representation"
    - "robust principal component analysis"

Connected through hyperedge HE-4 (induced by source paper "non negative representation based discriminative dictionary learning for face recognition"):
  with members:
    - "sparse collaborative or nonnegative representation which helps pattern classification"

Connected through hyperedge HE-5 (induced by source paper "a comprehensive survey for low rank regularization"):
  with members:
    - "bilinear factor matrix norm minimization for robust pca algorithms and applications"
    - "side information for face completion a robust pca approach"
    - "fast and accurate tensor completion with tensor trains a system identification approach"
    - "missing slice recovery for tensors using a low rank model in embedded space"
    - "scalable dense non rigid structure from motion a grassmannian perspective"
    - "multi channel weighted nuclear norm minimization for real color image denoising"
    - "large scale low rank matrix learning with nonconvex regularizers"
    - "side information in robust principal component analysis algorithms and applications"

Connected through hyperedge HE-6 (induced by source paper "incomplete multi view clustering via graph regularized matrix factorization"):
  with members:
    - "highly economized multi view binary compression for scalable image clustering"
    - "robust recovery of subspace structures by low rank representation"

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)