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: "the steerable graph laplacian and its application to filtering image data sets"
  Abstract: In recent years, improvements in various scientific image acquisition techniques gave rise to the need for adaptive processing methods aimed for large data-sets corrupted by noise and deformations. In this work, we consider data-sets of images sampled from an underlying low-dimensional manifold (i.e. an image-valued manifold), where the images are obtained through arbitrary planar rotations. To derive the mathematical framework for processing such data-sets, we introduce a graph Laplacian-like operator, termed steerable graph Laplacian (sGL), which extends the standard graph Laplacian (GL) by accounting for all (infinitely-many) planar rotations of all images. As it turns out, a properly normalized sGL converges to the Laplace-Beltrami operator on the low-dimensional manifold, with an improved convergence rate compared to the GL. Moreover, the sGL admits eigenfunctions of the form of Fourier modes multiplied by eigenvectors of certain matrices. For image data-sets corrupted by noise, we employ a subset of these eigenfunctions to "filter" the data-set, essentially using all images and their rotations simultaneously. We demonstrate our filtering framework by de-noising simulated single-particle cryo-EM image data-sets.

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"
    - "denoising and covariance estimation of single particle cryo em images"
    - "fast steerable principal component analysis"
    - "fourier bessel rotational invariant eigenimages"
    - "exact and stable recovery of rotations for robust synchronization"

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)