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: "large scale approximate kernel canonical correlation analysis"
  Abstract: Kernel canonical correlation analysis (KCCA) is a nonlinear multi-view representation learning technique with broad applicability in statistics and machine learning. Although there is a closed-form solution for the KCCA objective, it involves solving an $N\times N$ eigenvalue system where $N$ is the training set size, making its computational requirements in both memory and time prohibitive for large-scale problems. Various approximation techniques have been developed for KCCA. A commonly used approach is to first transform the original inputs to an $M$-dimensional random feature space so that inner products in the feature space approximate kernel evaluations, and then apply linear CCA to the transformed inputs. In many applications, however, the dimensionality $M$ of the random feature space may need to be very large in order to obtain a sufficiently good approximation; it then becomes challenging to perform the linear CCA step on the resulting very high-dimensional data matrices. We show how to use a stochastic optimization algorithm, recently proposed for linear CCA and its neural-network extension, to further alleviate the computation requirements of approximate KCCA. This approach allows us to run approximate KCCA on a speech dataset with $1.4$ million training samples and a random feature space of dimensionality $M=100000$ on a typical workstation.

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 "a general scoring rule for randomized kernel approximation with application to canonical correlation analysis"):
  with members:
    - "on sampling random features from empirical leverage scores implementation and theoretical guarantees"
    - "but how does it work in theory linear svm with random features"
    - "random fourier features for kernel ridge regression approximation bounds and statistical guarantees"
    - "data driven random fourier features using stein effect"
    - "stochastic approximation for canonical correlation analysis"
    - "orthogonal random features"
    - "efficient globally convergent stochastic optimization for canonical correlation analysis"
    - "on the equivalence between kernel quadrature rules and random feature expansions"

Connected through hyperedge HE-3 (induced by source paper "semi supervised deep representation learning for multi view problems"):
  with members:
    - "deep generalized canonical correlation analysis"
    - "a comprehensive survey on cross modal retrieval"
    - "deep learning in neural networks"
    - "deep linear discriminant analysis"
    - "adam a method for stochastic optimization"
    - "a survey on multi view learning"

Connected through hyperedge HE-4 (induced by source paper "deep variational canonical correlation analysis"):
  with members:
    - "deep variational information bottleneck"
    - "domain separation networks"
    - "infogan interpretable representation learning by information maximizing generative adversarial nets"
    - "draw a recurrent neural network for image generation"
    - "adam a method for stochastic optimization"
    - "multilingual distributed representations without word alignment"
    - "manifold relevance determination"
    - "a kernel method for canonical correlation analysis"

Connected through hyperedge HE-5 (induced by source paper "robust matrix elastic net based canonical correlation analysis an effective algorithm for multi view unsupervised learning"):
  with members:
    - "constrained low rank learning using least squares based regularization"
    - "unsupervised learning of long term motion dynamics for videos"
    - "a comprehensive survey on cross modal retrieval"
    - "efficient globally convergent stochastic optimization for canonical correlation analysis"
    - "scale up nonlinear component analysis with doubly stochastic gradients"
    - "a survey on multi view learning"
    - "trace lasso a trace norm regularization for correlated designs"

Connected through hyperedge HE-6 (induced by source paper "stochastic canonical correlation analysis"):
  with members:
    - "dropping convexity for more efficient and scalable online multiview learning"
    - "stochastic approximation for canonical correlation analysis"
    - "lazysvd even faster svd decomposition yet without agonizing pain"
    - "efficient algorithms for large scale generalized eigenvector computation and canonical correlation analysis"
    - "globally convergent stochastic optimization for canonical correlation analysis"
    - "faster eigenvector computation via shift and invert preconditioning"

Connected through hyperedge HE-7 (induced by source paper "multichannel kernel canonical correlation analysis for cross view person reidentification"):
  with members:
    - "transferring a semantic representation for person re identification and search"
    - "learning to rank in person re identification with metric ensembles"
    - "person re identification with correspondence structure learning"
    - "person re identification by local maximal occurrence representation and metric learning"
    - "deep metric learning for practical person re identification"

Connected through hyperedge HE-8 (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"
    - "nonparametric canonical correlation analysis"
    - "stochastic optimization for deep cca via nonlinear orthogonal iterations"
    - "discriminative features via generalized eigenvectors"
    - "efficient dimensionality reduction for canonical correlation analysis"
    - "nearly linear time algorithms for graph partitioning graph sparsification and solving linear systems"

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.LG(Machine Learning)