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: "3d steerable cnns learning rotationally equivariant features in volumetric data"
  Abstract: We present a convolutional network that is equivariant to rigid body motions. The model uses scalar-, vector-, and tensor fields over 3D Euclidean space to represent data, and equivariant convolutions to map between such representations. These SE(3)-equivariant convolutions utilize kernels which are parameterized as a linear combination of a complete steerable kernel basis, which is derived analytically in this paper. We prove that equivariant convolutions are the most general equivariant linear maps between fields over R^3. Our experimental results confirm the effectiveness of 3D Steerable CNNs for the problem of amino acid propensity prediction and protein structure classification, both of which have inherent SE(3) symmetry.

Connected through hyperedge HE-1 (induced by source paper "on learning sets of symmetric elements"):
  with members:
    - "universal invariant and equivariant graph neural networks"
    - "deep models for relational databases"
    - "incidence networks for geometric deep learning"
    - "on the limitations of representing functions on sets"
    - "permutation invariant feature restructuring for correlation aware image set based recognition"
    - "gauge equivariant convolutional networks and the icosahedral cnn"
    - "multiview aggregation for learning category specific shape reconstruction"
    - "on the universality of invariant networks"

Connected through hyperedge HE-2 (induced by source paper "a rotation invariant framework for deep point cloud analysis"):
  with members:
    - "deepicp an end to end deep neural network for 3d point cloud registration"
    - "total denoising unsupervised learning of 3d point cloud cleaning"
    - "deep unsupervised learning of 3d point clouds via graph topology inference and filtering"
    - "kpconv flexible and deformable convolution for point clouds"
    - "monte carlo convolution for learning on non uniformly sampled point clouds"
    - "pu gan a point cloud upsampling adversarial network"
    - "rotation invariant convolutions for 3d point clouds deep learning"
    - "pointnetlk robust efficient point cloud registration using pointnet"

Connected through hyperedge HE-3 (induced by source paper "set2graph learning graphs from sets"):
  with members:
    - "universal invariant and equivariant graph neural networks"
    - "meta learning to cluster"
    - "universal approximations of permutation invariant equivariant functions by deep neural networks"
    - "on the universality of invariant networks"
    - "provably powerful graph networks"
    - "on universal equivariant set networks"
    - "on the equivalence between graph isomorphism testing and function approximation with gnns"
    - "clustering with deep learning taxonomy and new methods"

Connected through hyperedge HE-4 (induced by source paper "gauge equivariant mesh cnns anisotropic convolutions on geometric graphs"):
  with members:
    - "deep scale spaces equivariance over scale"
    - "general e 2 equivariant steerable cnns"
    - "gauge equivariant convolutional networks and the icosahedral cnn"
    - "spherical u net on cortical surfaces methods and applications"
    - "spiralnet a fast and highly efficient mesh convolution operator"
    - "zernet convolutional neural networks on arbitrary surfaces via zernike local tangent space estimation"
    - "parallel transport convolution a new tool for convolutional neural networks on manifolds"
    - "roto translation covariant convolutional networks for medical image analysis"

Connected through hyperedge HE-5 (induced by source paper "provably scale covariant continuous hierarchical networks based on scale normalized differential expressions coupled in cascade"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "blurring the line between structure and learning to optimize and adapt receptive fields"
    - "provably scale covariant networks from oriented quasi quadrature measures in cascade"
    - "drop an octave reducing spatial redundancy in convolutional neural networks with octave convolution"
    - "elastic improving cnns with dynamic scaling policies"
    - "from bow to cnn two decades of texture representation for texture classification"
    - "espnet efficient spatial pyramid of dilated convolutions for semantic segmentation"
    - "gabor convolutional networks"

Connected through hyperedge HE-6 (induced by source paper "representation learning on unit ball with 3d roto translational equivariance"):
  with members:
    - "blended convolution and synthesis for efficient discrimination of 3d shapes"
    - "n body networks a covariant hierarchical neural network architecture for learning atomic potentials"
    - "adversarial training of variational auto encoders for high fidelity image generation"
    - "attention based deep multiple instance learning"
    - "splatnet sparse lattice networks for point cloud processing"
    - "so net self organizing network for point cloud analysis"
    - "cubenet equivariance to 3d rotation and translation"
    - "dynamic graph cnn for learning on point clouds"

Connected through hyperedge HE-7 (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-8 (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"

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)