human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "cryo electron microscopy image analysis using multi frequency vector diffusion maps"):
  Abstract: Cryo-electron microscopy (EM) single particle reconstruction is an entirely general technique for 3D structure determination of macromolecular complexes. However, because the images are taken at low electron dose, it is extremely hard to visualize the individual particle with low contrast and high noise level. In this paper, we propose a novel approach called multi-frequency vector diffusion maps (MFVDM) to improve the efficiency and accuracy of cryo-EM 2D image classification and denoising. This framework incorporates different irreducible representations of the estimated alignment between similar images. In addition, we propose a graph filtering scheme to denoise the images using the eigenvalues and eigenvectors of the MFVDM matrices. Through both simulated and publicly available real data, we demonstrate that our proposed method is efficient and robust to noise compared with the state-of-the-art cryo-EM 2D class averaging and image restoration algorithms.

Includes members:
  Member "apple picker automatic particle picking a low effort cryo em framework":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "image based size analysis of agglomerated and partially sintered particles via convolutional neural networks")
  Member "the steerable graph laplacian and its application to filtering image data sets":
    also connected through other hyperedges:
      (none)
  Member "denoising and covariance estimation of single particle cryo em images":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "single particle cryo electron microscopy mathematical theory computational challenges and opportunities")
  Member "fast steerable principal component analysis":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "single particle cryo electron microscopy mathematical theory computational challenges and opportunities")
  Member "fourier bessel rotational invariant eigenimages":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the steerable graph laplacian and its application to filtering image data sets")
  Member "exact and stable recovery of rotations for robust synchronization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on the distribution of minima in intrinsic metric rotation averaging")

Question: Please tell me which class the source paper 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)