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: "fourier bessel rotational invariant eigenimages"
  Abstract: We present an efficient and accurate algorithm for principal component analysis (PCA) of a large set of two-dimensional images and, for each image, the set of its uniform rotations in the plane and its reflection. The algorithm starts by expanding each image, originally given on a Cartesian grid, in the Fourier–Bessel basis for the disk. Because the images are essentially band limited in the Fourier domain, we use a sampling criterion to truncate the Fourier–Bessel expansion such that the maximum amount of information is preserved without the effect of aliasing. The constructed covariance matrix is invariant to rotation and reflection and has a special block diagonal structure. PCA is efficiently done for each block separately. This Fourier–Bessel-based PCA detects more meaningful eigenimages and has improved denoising capability compared to traditional PCA for a finite number of noisy images.

Connected through hyperedge HE-1 (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"
    - "fast steerable principal component analysis"
    - "exact and stable recovery of rotations for robust synchronization"

Connected through hyperedge HE-2 (induced by source paper "the steerable graph laplacian and its application to filtering image data sets"):
  with members:
    - "steerable principal components for space frequency localized images"
    - "on the optimality of shape and data representation in the spectral domain"

Connected through hyperedge HE-3 (induced by source paper "mse estimates for multitaper spectral estimation and off grid compressive sensing"):
  with members:
    - "time frequency and time scale analysis of deformed stationary processes with application to non stationary sound modeling"
    - "approximating sampled sinusoids and multiband signals using multiband modulated dpss dictionaries"
    - "compressive sensing of analog signals using discrete prolate spheroidal sequences"

Connected through hyperedge HE-4 (induced by source paper "steerable principal components for space frequency localized images"):
  with members:
    - "fast steerable principal component analysis"
    - "recurrence relations and fast algorithms"

Connected through hyperedge HE-5 (induced by source paper "fast steerable principal component analysis"):
  with members:
    - "orthogonal matrix retrieval in cryo electron microscopy"
    - "fast algorithms for spherical harmonic expansions"

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)