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: "vectorial dimension reduction for tensors based on bayesian inference"
  Abstract: Dimensionality reduction for high-order tensors is a challenging problem. In conventional approaches, higher order tensors are `vectorized` via Tucker decomposition to obtain lower order tensors. This will destroy the inherent high-order structures or resulting in undesired tensors, respectively. This paper introduces a probabilistic vectorial dimensionality reduction model for tensorial data. The model represents a tensor by employing a linear combination of same order basis tensors, thus it offers a mechanism to directly reduce a tensor to a vector. Under this expression, the projection base of the model is based on the tensor CandeComp/PARAFAC (CP) decomposition and the number of free parameters in the model only grows linearly with the number of modes rather than exponentially. A Bayesian inference has been established via the variational EM approach. A criterion to set the parameters (factor number of CP decomposition and the number of extracted features) is empirically given. The model outperforms several existing PCA-based methods and CP decomposition on several publicly available databases in terms of classification and clustering accuracy.

Connected through hyperedge HE-1 (induced by source paper "tensor networks for latent variable analysis higher order canonical polyadic decomposition"):
  with members:
    - "tensor networks for dimensionality reduction and large scale optimization part 2 applications and future perspectives"
    - "tensor networks for dimensionality reduction and large scale optimization part 1 low rank tensor decompositions"
    - "bayesian robust tensor factorization for incomplete multiway data"
    - "speeding up convolutional neural networks with low rank expansions"
    - "smoothed analysis of tensor decompositions"
    - "low complexity damped gauss newton algorithms for candecomp parafac"

Connected through hyperedge HE-2 (induced by source paper "simultaneously learning neighborship and projection matrix for supervised dimensionality reduction"):
  with members:
    - "cascade learning by optimally partitioning"
    - "learning sampling distributions for efficient object detection"
    - "cascaded subpatch networks for effective cnns"
    - "convolution in convolution for network in network"
    - "deep linear discriminant analysis"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"
    - "from manifold to manifold geometry aware dimensionality reduction for spd matrices"

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)