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: "multilinear principal component analysis network for tensor object classification"
  Abstract: The recently proposed principal component analysis network (PCANet) has been proved high performance for visual content classification. In this letter, we develop a tensorial extension of PCANet, namely, multilinear principal analysis component network (MPCANet), for tensor object classification. Compared to PCANet, the proposed MPCANet uses the spatial structure and the relationship between each dimension of tensor objects much more efficiently. Experiments were conducted on different visual content datasets including UCF sports action video sequences database and UCF11 database. The experimental results have revealed that the proposed MPCANet achieves higher classification accuracy than PCANet for tensor object classification.

Connected through hyperedge HE-1 (induced by source paper "stacking based deep neural network deep analytic network for pattern classification"):
  with members:
    - "stacking based deep neural network deep analytic network on convolutional spectral histogram features"
    - "deep forest towards an alternative to deep neural networks"
    - "all you need is a good init"
    - "deepid3 face recognition with very deep neural networks"
    - "deep residual learning for image recognition"
    - "facenet a unified embedding for face recognition and clustering"
    - "pcanet a simple deep learning baseline for image classification"
    - "how transferable are features in deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "principal component analysis with tensor train subspace"):
  with members:
    - "tensor train neighborhood preserving embedding"
    - "efficient low rank tensor ring completion"
    - "tensor completion by alternating minimization under the tensor train tt model"
    - "tensor networks for dimensionality reduction and large scale optimization part 1 low rank tensor decompositions"
    - "era of big data processing a new approach via tensor networks and tensor decompositions"
    - "generalized principal component analysis gpca"

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)