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: "kernel principal component analysis and its applications in face recognition and active shape models"
  Abstract: Principal component analysis (PCA) is a popular tool for linear dimensionality reduction and feature extraction. Kernel PCA is the nonlinear form of PCA, which better exploits the complicated spatial structure of high-dimensional features. In this paper, we first review the basic ideas of PCA and kernel PCA. Then we focus on the reconstruction of pre-images for kernel PCA. We also give an introduction on how PCA is used in active shape models (ASMs), and discuss how kernel PCA can be applied to improve traditional ASMs. Then we show some experimental results to compare the performance of kernel PCA and standard PCA for classification problems. We also implement the kernel PCA-based ASMs, and use it to construct human face models.

Connected through hyperedge HE-1 (induced by source paper "empirical evaluation of kernel pca approximation methods in classification tasks"):
  with members:
    - "streaming kernel principal component analysis"
    - "simple and deterministic matrix sketching"

Connected through hyperedge HE-2 (induced by source paper "learning discriminative features using encoder decoder type deep neural nets"):
  with members:
    - "deep lambertian networks"
    - "deep big simple neural nets for handwritten digit recognition"
    - "a mirroring theorem and its application to a new method of unsupervised hierarchical pattern classification"
    - "pattern recognition and memory mapping using mirroring neural networks"

Connected through hyperedge HE-3 (induced by source paper "feature learning by multidimensional scaling and its applications in object recognition"):
  with members:
    - "building high level features using large scale unsupervised learning"

Connected through hyperedge HE-4 (induced by source paper "diffusion map for clustering fmri spatial maps extracted by independent component analysis"):
  with members:
    - "a tutorial on spectral clustering"

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)