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: "intertwiners between induced representations with applications to the theory of equivariant neural networks"
  Abstract: Group equivariant and steerable convolutional neural networks (regular and steerable G-CNNs) have recently emerged as a very effective model class for learning from signal data such as 2D and 3D images, video, and other data where symmetries are present. In geometrical terms, regular G-CNNs represent data in terms of scalar fields ("feature channels"), whereas the steerable G-CNN can also use vector or tensor fields ("capsules") to represent data. In algebraic terms, the feature spaces in regular G-CNNs transform according to a regular representation of the group G, whereas the feature spaces in Steerable G-CNNs transform according to the more general induced representations of G. In order to make the network equivariant, each layer in a G-CNN is required to intertwine between the induced representations associated with its input and output space. #R##N#In this paper we present a general mathematical framework for G-CNNs on homogeneous spaces like Euclidean space or the sphere. We show, using elementary methods, that the layers of an equivariant network are convolutional if and only if the input and output feature spaces transform according to an induced representation. This result, which follows from G.W. Mackey's abstract theory on induced representations, establishes G-CNNs as a universal class of equivariant network architectures, and generalizes the important recent work of Kondor & Trivedi on the intertwiners between regular representations.

Connected through hyperedge HE-1 (induced by source paper "general e 2 equivariant steerable cnns"):
  with members:
    - "spherical cnns on unstructured grids"
    - "gauge equivariant convolutional networks and the icosahedral cnn"
    - "learning to convolve a generalized weight tying approach"
    - "n body networks a covariant hierarchical neural network architecture for learning atomic potentials"
    - "land cover mapping at very high resolution with rotation equivariant cnns towards small yet accurate models"
    - "roto translation covariant convolutional networks for medical image analysis"
    - "multi directional geodesic neural networks via equivariant convolution"
    - "3d g cnns for pulmonary nodule detection"

Connected through hyperedge HE-2 (induced by source paper "discrete rotation equivariance for point cloud recognition"):
  with members:
    - "n body networks a covariant hierarchical neural network architecture for learning atomic potentials"
    - "spidercnn deep learning on point sets with parameterized convolutional filters"
    - "deep sets"
    - "so net self organizing network for point cloud analysis"
    - "cubenet equivariance to 3d rotation and translation"
    - "tensor field networks rotation and translation equivariant neural networks for 3d point clouds"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"
    - "polar transformer networks"

Connected through hyperedge HE-3 (induced by source paper "deep models for relational databases"):
  with members:
    - "gauge equivariant convolutional networks and the icosahedral cnn"
    - "on the universality of invariant networks"
    - "neural feature learning from relational database"
    - "graph convolutional neural networks for web scale recommender systems"
    - "deep sets"
    - "relational inductive biases deep learning and graph networks"
    - "adaptive sampling towards fast graph representation learning"
    - "fastgcn fast learning with graph convolutional networks via importance sampling"

Connected through hyperedge HE-4 (induced by source paper "gauge equivariant convolutional networks and the icosahedral cnn"):
  with members:
    - "spherical cnns on unstructured grids"
    - "parallel transport convolution a new tool for convolutional neural networks on manifolds"
    - "roto translation covariant convolutional networks for medical image analysis"
    - "multi directional geodesic neural networks via equivariant convolution"
    - "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"
    - "invariant and equivariant graph networks"

Connected through hyperedge HE-5 (induced by source paper "equivariant entity relationship networks"):
  with members:
    - "gauge equivariant convolutional networks and the icosahedral cnn"
    - "graph convolutional neural networks for web scale recommender systems"
    - "learning features for relational data"
    - "relational inductive biases deep learning and graph networks"
    - "adaptive sampling towards fast graph representation learning"
    - "fastgcn fast learning with graph convolutional networks via importance sampling"
    - "invariant and equivariant graph networks"
    - "covariant compositional networks for learning graphs"

Connected through hyperedge HE-6 (induced by source paper "avt unsupervised learning of transformation equivariant representations by autoencoding variational transformations"):
  with members:
    - "aet vs aed unsupervised representation learning by auto encoding transformations rather than data"
    - "learning generalized transformation equivariant representations via autoencoding transformations"
    - "deep clustering for unsupervised learning of visual features"
    - "group equivariant capsule networks"
    - "unsupervised representation learning by predicting image rotations"
    - "loss sensitive generative adversarial networks on lipschitz densities"
    - "dynamic routing between capsules"
    - "scaling the scattering transform deep hybrid networks"

Connected through hyperedge HE-7 (induced by source paper "incidence networks for geometric deep learning"):
  with members:
    - "deep models for relational databases"
    - "gauge equivariant convolutional networks and the icosahedral cnn"
    - "on the universality of invariant networks"
    - "on the equivalence between graph isomorphism testing and function approximation with gnns"
    - "weisfeiler and leman go neural higher order graph neural networks"
    - "relational inductive biases deep learning and graph networks"
    - "invariant and equivariant graph networks"
    - "covariant compositional networks for learning graphs"

Connected through hyperedge HE-8 (induced by source paper "covariance in physics and convolutional neural networks"):
  with members:
    - "gauge equivariant convolutional networks and the icosahedral cnn"
    - "3d g cnns for pulmonary nodule detection"
    - "cubenet equivariance to 3d rotation and translation"
    - "3d steerable cnns learning rotationally equivariant features in volumetric data"
    - "learning steerable filters for rotation equivariant cnns"
    - "harmonic networks deep translation and rotation equivariance"
    - "group equivariant convolutional networks"

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.LG(Machine Learning)