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: "multi fold gabor pca and ica filter convolution descriptor for face recognition"
  Abstract: This paper devises a new means of filter diversification, dubbed multi-fold filter convolution (   $\mathcal {M}$   -FFC), for face recognition. On the assumption that    $\mathcal {M}$   -FFC receives single-scale Gabor filters of varying orientations as input, these filters are self-cross convolved by    $\mathcal {M}$   -fold to instantiate a filter offspring set. The    $\mathcal {M}$   -FFC flexibility also permits cross convolution amongst Gabor filters and other filter banks of profoundly dissimilar traits, e.g., principal component analysis (PCA) filters and independent component analysis (ICA) filters. The 2-FFC of Gabor, PCA, and ICA filters thus yields three offspring sets: 1) Gabor filters solely; 2) Gabor-PCA filters; and 3) Gabor-ICA filters, to render the learning-free and the learning-based 2-FFC descriptors. To facilitate a sensible Gabor filter selection for    $\mathcal {M}$   -FFC, the 40 multi-scale, multi-orientation Gabor filters are condensed into eight elementary filters. Aside from that, an average histogram pooling operator is employed to leverage the 2-FFC histogram features, prior to the final whitening PCA compression. The empirical results substantiate that the 2-FFC descriptors prevail over, or on par with, other face descriptors on both identification and verification tasks.

Connected through hyperedge HE-1 (induced by source paper "lglg wpca an effective texture based method for face recognition"):
  with members:
    - "covariance pooling for facial expression recognition"
    - "learning a high fidelity pose invariant model for high resolution face frontalization"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "squeeze and excitation networks"
    - "towards faster training of global covariance pooling networks by iterative matrix square root normalization"
    - "dimensionality reduction on spd manifolds the emergence of geometry aware methods"
    - "kernel methods on approximate infinite dimensional covariance operators for image classification"

Connected through hyperedge HE-2 (induced by source paper "stacking based deep neural network deep analytic network on convolutional spectral histogram features"):
  with members:
    - "all you need is a good init"
    - "pcanet a simple deep learning baseline for image classification"
    - "striving for simplicity the all convolutional net"
    - "spatial pyramid pooling in deep convolutional networks for visual recognition"
    - "fractional max pooling"
    - "issues in stacked generalization"

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)