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: "template matching via densities on the roto translation group"
  Abstract: We propose a template matching method for the detection of 2D image objects that are characterized by orientation patterns. Our method is based on data representations via orientation scores, which are functions on the space of positions and orientations, and which are obtained via a wavelet-type transform. This new representation allows us to detect orientation patterns in an intuitive and direct way, namely via cross-correlations. Additionally, we propose a generalized linear regression framework for the construction of suitable templates using smoothing splines. Here, it is important to recognize a curved geometry on the position-orientation domain, which we identify with the Lie group SE(2): the roto-translation group. Templates are then optimized in a B-spline basis, and smoothness is defined with respect to the curved geometry. We achieve state-of-the-art results on three different applications: detection of the optic nerve head in the retina (99.83 percent success rate on 1,737 images), of the fovea in the retina (99.32 percent success rate on 1,616 images), and of the pupil in regular camera images (95.86 percent on 1,521 images). The high performance is due to inclusion of both intensity and orientation features with effective geometric priors in the template matching. Moreover, our method is fast due to a cross-correlation based matching approach.

Connected through hyperedge HE-1 (induced by source paper "drmime differentiable mutual information and matrix exponential for multi resolution image registration"):
  with members:
    - "airlab autograd image registration laboratory"
    - "mine mutual information neural estimation"
    - "deep image homography estimation"

Connected through hyperedge HE-2 (induced by source paper "b spline cnns on lie groups"):
  with members:
    - "deep scale spaces equivariance over scale"
    - "gauge equivariant convolutional networks and the icosahedral cnn"
    - "scale aware trident networks for object detection"
    - "roto translation covariant convolutional networks for medical image analysis"
    - "group equivariant capsule networks"
    - "3d g cnns for pulmonary nodule detection"
    - "cubenet equivariance to 3d rotation and translation"
    - "tensor field networks rotation and translation equivariant neural networks for 3d point clouds"

Connected through hyperedge HE-3 (induced by source paper "ddnet cartesian polar dual domain network for the joint optic disc and cup segmentation"):
  with members:
    - "joint optic disc and cup segmentation based on multi label deep network and polar transformation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "cbam convolutional block attention module"
    - "polar transformer networks"
    - "optic disc and cup segmentation methods for glaucoma detection with modification of u net convolutional neural network"
    - "deep retinal image understanding"
    - "group equivariant convolutional networks"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-4 (induced by source paper "roto translation covariant convolutional networks for medical image analysis"):
  with members:
    - "learning steerable filters for rotation equivariant cnns"
    - "harmonic networks deep translation and rotation equivariance"
    - "exploiting cyclic symmetry in convolutional neural networks"
    - "warped convolutions efficient invariance to spatial transformations"
    - "group equivariant convolutional networks"
    - "assessment of algorithms for mitosis detection in breast cancer histopathology images"
    - "generic deep networks with wavelet scattering"
    - "learning invariant representations with local transformations"

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)