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: "multi view intact space learning"
  Abstract: It is practical to assume that an individual view is unlikely to be sufficient for effective multi-view learning. Therefore, integration of multi-view information is both valuable and necessary. In this paper, we propose the Multi-view Intact Space Learning (MISL) algorithm, which integrates the encoded complementary information in multiple views to discover a latent intact representation of the data. Even though each view on its own is insufficient, we show theoretically that by combing multiple views we can obtain abundant information for latent intact space learning. Employing the Cauchy loss (a technique used in statistical learning) as the error measurement strengthens robustness to outliers. We propose a new definition of multi-view stability and then derive the generalization error bound based on multi-view stability and Rademacher complexity, and show that the complementarity between multiple views is beneficial for the stability and generalization. MISL is efficiently optimized using a novel Iteratively Reweight Residuals (IRR) technique, whose convergence is theoretically analyzed. Experiments on synthetic data and real-world datasets demonstrate that MISL is an effective and promising algorithm for practical applications.

Connected through hyperedge HE-1 (induced by source paper "deep multi view learning using neuron wise correlation maximizing regularizers"):
  with members:
    - "dominant set clustering and pooling for multi view 3d object recognition"
    - "depth cnns for rgb d scene recognition learning from scratch better than transferring from rgb cnns"
    - "theory of deep learning iib optimization properties of sgd"
    - "data dependent stability of stochastic gradient descent"
    - "aggregated residual transformations for deep neural networks"
    - "understanding deep learning requires rethinking generalization"
    - "identity mappings in deep residual networks"
    - "rotationnet joint object categorization and pose estimation using multiviews from unsupervised viewpoints"

Connected through hyperedge HE-2 (induced by source paper "robust subspace clustering by cauchy loss function"):
  with members:
    - "sce a manifold regularized set covering method for data partitioning"
    - "constructing the l2 graph for robust subspace learning and subspace clustering"
    - "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-3 (induced by source paper "a multi view dimensionality reduction algorithm based on smooth representation model"):
  with members:
    - "a survey of multi view representation learning"
    - "cycle consistent deep generative hashing for cross modal retrieval"
    - "multiview spectral clustering via structured low rank matrix factorization"
    - "where and when to look deep siamese attention networks for video based person re identification"
    - "effective multi query expansions collaborative deep networks for robust landmark retrieval"
    - "beyond low rank representations orthogonal clustering basis reconstruction with optimized graph structure for multi view spectral clustering"
    - "iterative views agreement an iterative low rank based structured optimization method to multi view spectral clustering"
    - "correlation adaptive subspace segmentation by trace lasso"

Connected through hyperedge HE-4 (induced by source paper "latent complete row space recovery for multi view subspace clustering"):
  with members:
    - "robust subspace clustering with compressed data"
    - "multi view low rank sparse subspace clustering"
    - "essential tensor learning for multi view spectral clustering"
    - "subspace clustering by block diagonal representation"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "query adaptive hash code ranking for large scale multi view visual search"):
  with members:
    - "projection bank from high dimensional data to medium length binary codes"
    - "hashing on nonlinear manifolds"
    - "simultaneous feature learning and hash coding with deep neural networks"
    - "supervised hashing using graph cuts and boosted decision trees"
    - "inductive hashing on manifolds"

Connected through hyperedge HE-6 (induced by source paper "correlated logistic model with elastic net regularization for multilabel image classification"):
  with members:
    - "video tracking using learned hierarchical features"
    - "return of the devil in the details delving deep into convolutional nets"
    - "matconvnet convolutional neural networks for matlab"
    - "maximum margin output coding"
    - "multi label prediction via compressed sensing"

Connected through hyperedge HE-7 (induced by source paper "simultaneous spectral spatial feature selection and extraction for hyperspectral images"):
  with members:
    - "advances in hyperspectral image classification earth monitoring with statistical learning methods"
    - "representation learning a review and new perspectives"

Connected through hyperedge HE-8 (induced by source paper "exploring representativeness and informativeness for active learning"):
  with members:
    - "hierarchical subquery evaluation for active learning on a graph"
    - "efficient active learning of halfspaces an aggressive approach"

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)