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: "fast steerable principal component analysis"
  Abstract: Cryo-electron microscopy nowadays often requires the analysis of hundreds of thousands of 2D images as large as a few hundred pixels in each direction. Here we introduce an algorithm that efficiently and accurately performs principal component analysis (PCA) for a large set of two-dimensional images, and, for each image, the set of its uniform rotations in the plane and their reflections. For a dataset consisting of $n$ images of size $L \times L$ pixels, the computational complexity of our algorithm is $O(nL^3 + L^4)$, while existing algorithms take $O(nL^4)$. The new algorithm computes the expansion coefficients of the images in a Fourier-Bessel basis efficiently using the non-uniform fast Fourier transform. We compare the accuracy and efficiency of the new algorithm with traditional PCA and existing algorithms for steerable PCA.

Connected through hyperedge HE-1 (induced by source paper "single particle cryo electron microscopy mathematical theory computational challenges and opportunities"):
  with members:
    - "unsupervised particle sorting for high resolution single particle cryo em"
    - "a common lines approach for ab initio modeling of cyclically symmetric molecules"
    - "multi target detection with application to cryo electron microscopy"
    - "apple picker automatic particle picking a low effort cryo em framework"
    - "toward single particle reconstruction without particle picking breaking the detection limit"
    - "estimation in the group action channel"
    - "bispectrum inversion with application to multireference alignment"
    - "heterogeneous multireference alignment a single pass approach"

Connected through hyperedge HE-2 (induced by source paper "unsupervised co learning on mathcal g manifolds across irreducible representations"):
  with members:
    - "cryo electron microscopy image analysis using multi frequency vector diffusion maps"
    - "a survey of multi view representation learning"
    - "multi frequency phase synchronization"
    - "multi frequency vector diffusion maps"
    - "a study of the classification of low dimensional data with supervised manifold learning"
    - "a survey on multi view learning"
    - "exact matrix completion via convex optimization"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-3 (induced by source paper "cryo electron microscopy image analysis using multi frequency vector diffusion maps"):
  with members:
    - "apple picker automatic particle picking a low effort cryo em framework"
    - "the steerable graph laplacian and its application to filtering image data sets"
    - "denoising and covariance estimation of single particle cryo em images"
    - "fourier bessel rotational invariant eigenimages"
    - "exact and stable recovery of rotations for robust synchronization"

Connected through hyperedge HE-4 (induced by source paper "multi frequency vector diffusion maps"):
  with members:
    - "multi frequency phase synchronization"
    - "revisiting semi supervised learning with graph embeddings"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-5 (induced by source paper "steerable e pca rotationally invariant exponential family pca"):
  with members:
    - "steerable principal components for space frequency localized images"
    - "poisson noise reduction with non local pca"

Connected through hyperedge HE-6 (induced by source paper "toward single particle reconstruction without particle picking breaking the detection limit"):
  with members:
    - "bispectrum inversion with application to multireference alignment"
    - "anisotropic twicing for single particle reconstruction using autocorrelation analysis"
    - "heterogeneous multireference alignment a single pass approach"
    - "steerable principal components for space frequency localized images"
    - "multireference alignment is easier with an aperiodic translation distribution"
    - "manopt a matlab toolbox for optimization on manifolds"

Connected through hyperedge HE-7 (induced by source paper "steerable principal components for space frequency localized images"):
  with members:
    - "fourier bessel rotational invariant eigenimages"
    - "recurrence relations and fast algorithms"

Connected through hyperedge HE-8 (induced by source paper "denoising and covariance estimation of single particle cryo em images"):
  with members:
    - "covariance estimation using conjugate gradient for 3d classification in cryo em"
    - "orthogonal matrix retrieval in cryo electron microscopy"

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)