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: "efficient dimensionality reduction for canonical correlation analysis"
  Abstract: We present a fast algorithm for approximate Canonical Correlation Analysis (CCA). Given a pair of tall-and-thin matrices, the proposed algorithm first employs a randomized dimensionality reduction transform to reduce the size of the input matrices, and then applies any CCA algorithm to the new pair of matrices. The algorithm computes an approximate CCA to the original pair of matrices with provable guarantees, while requiring asymptotically less operations than the state-of-the-art exact algorithms.

Connected through hyperedge HE-1 (induced by source paper "a survey of multi view representation learning"):
  with members:
    - "word2visualvec cross media retrieval by visual feature prediction"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "efficient algorithms for large scale generalized eigenvector computation and canonical correlation analysis"
    - "rotationnet joint object categorization and pose estimation using multiviews from unsupervised viewpoints"
    - "globally convergent stochastic optimization for canonical correlation analysis"
    - "word2visualvec image and video to sentence matching by visual feature prediction"
    - "convolutional two stream network fusion for video action recognition"
    - "deep cross modal hashing"

Connected through hyperedge HE-2 (induced by source paper "efficient algorithms for large scale generalized eigenvector computation and canonical correlation analysis"):
  with members:
    - "globally convergent stochastic optimization for canonical correlation analysis"
    - "robust shift and invert preconditioning faster and more sample efficient algorithms for eigenvector computation"
    - "randomized block krylov methods for stronger and faster approximate singular value decomposition"
    - "large scale approximate kernel canonical correlation analysis"
    - "nonparametric canonical correlation analysis"
    - "stochastic optimization for deep cca via nonlinear orthogonal iterations"
    - "discriminative features via generalized eigenvectors"
    - "nearly linear time algorithms for graph partitioning graph sparsification and solving linear systems"

Connected through hyperedge HE-3 (induced by source paper "asymmetrically weighted cca and hierarchical kernel sentence embedding for image text retrieval"):
  with members:
    - "deep residual learning for image recognition"
    - "phrase based image captioning"
    - "stochastic optimization for deep cca via nonlinear orthogonal iterations"
    - "very deep convolutional networks for large scale image recognition"
    - "unifying visual semantic embeddings with multimodal neural language models"
    - "explain images with multimodal recurrent neural networks"
    - "deep fragment embeddings for bidirectional image sentence mapping"
    - "from captions to visual concepts and back"

Connected through hyperedge HE-4 (induced by source paper "large scale approximate kernel canonical correlation analysis"):
  with members:
    - "the fast convergence of incremental pca"
    - "scale up nonlinear component analysis with doubly stochastic gradients"
    - "stochastic optimization for deep cca via nonlinear orthogonal iterations"
    - "a stochastic pca and svd algorithm with an exponential convergence rate"
    - "how to scale up kernel methods to be as good as deep neural nets"
    - "revisiting the nystrom method for improved large scale machine learning"
    - "adaptive canonical correlation analysis based on matrix manifolds"
    - "explicit approximations of the gaussian kernel"

Connected through hyperedge HE-5 (induced by source paper "toward a unified theory of sparse dimensionality reduction in euclidean space"):
  with members:
    - "randomized sketches of convex programs with sharp guarantees"
    - "dimensionality reduction with subgaussian matrices a unified theory"
    - "the johnson lindenstrauss lemma is optimal for linear dimensionality reduction"
    - "dimensionality reduction for k means clustering and low rank approximation"
    - "uniform recovery of fusion frame structured sparse signals"
    - "new analysis of manifold embeddings and signal recovery from compressive measurements"
    - "subspace embeddings and ell_p regression using exponential random variables"
    - "on model based rip 1 matrices"

Connected through hyperedge HE-6 (induced by source paper "spectral clustering via the power method provably"):
  with members:
    - "sketching as a tool for numerical linear algebra"
    - "dimensionality reduction for k means clustering and low rank approximation"
    - "deterministic feature selection for k means clustering"
    - "low rank approximation and regression in input sparsity time"
    - "improved matrix algorithms via the subsampled randomized hadamard transform"
    - "random projections for k means clustering"
    - "approaching optimality for solving sdd systems"
    - "random projections for the nonnegative least squares problem"

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.DS(Data Structures and Algorithms)