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 "local rotation invariance in 3d cnns"):
  Abstract: Locally Rotation Invariant (LRI) image analysis was shown to be fundamental in many applications and in particular in medical imaging where local structures of tissues occur at arbitrary rotations. LRI constituted the cornerstone of several breakthroughs in texture analysis, including Local Binary Patterns (LBP), Maximum Response 8 (MR8) and steerable filterbanks. Whereas globally rotation invariant Convolutional Neural Networks (CNN) were recently proposed, LRI was very little investigated in the context of deep learning. LRI designs allow learning filters accounting for all orientations, which enables a drastic reduction of trainable parameters and training data when compared to standard 3D CNNs. In this paper, we propose and compare several methods to obtain LRI CNNs with directional sensitivity. Two methods use orientation channels (responses to rotated kernels), either by explicitly rotating the kernels or using steerable filters. These orientation channels constitute a locally rotation equivariant representation of the data. Local pooling across orientations yields LRI image analysis. Steerable filters are used to achieve a fine and efficient sampling of 3D rotations as well as a reduction of trainable parameters and operations, thanks to a parametric representations involving solid Spherical Harmonics (SH), which are products of SH with associated learned radial profiles.Finally, we investigate a third strategy to obtain LRI based on rotational invariants calculated from responses to a learned set of solid SHs. The proposed methods are evaluated and compared to standard CNNs on 3D datasets including synthetic textured volumes composed of rotated patterns, and pulmonary nodule classification in CT. The results show the importance of LRI image analysis while resulting in a drastic reduction of trainable parameters, outperforming standard 3D CNNs trained with data augmentation.

Includes members:
  Member "b spline cnns on lie groups":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "roto translation equivariant convolutional networks application to histopathology image analysis")
  Member "from bow to cnn two decades of texture representation for texture classification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "demographic bias in biometrics a survey on an emerging challenge")
  Member "rotdcf decomposition of convolutional filters for rotation equivariant deep networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "attentive group equivariant convolutional networks")
  Member "roto translation covariant convolutional networks for medical image analysis":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "attentive group equivariant convolutional networks")
  Member "rotational 3d texture classification using group equivariant cnns":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "provably scale covariant continuous hierarchical networks based on scale normalized differential expressions coupled in cascade")
  Member "cubenet equivariance to 3d rotation and translation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on learning sets of symmetric elements")
  Member "a general theory of equivariant cnns on homogeneous spaces":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on learning sets of symmetric elements")
  Member "learning steerable filters for rotation equivariant cnns":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "attentive group equivariant convolutional networks")

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)