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: "parallel transport convolution a new tool for convolutional neural networks on manifolds"
  Abstract: Convolution has been playing a prominent role in various applications in science and engineering for many years. It is the most important operation in convolutional neural networks. There has been a recent growth of interests of research in generalizing convolutions on curved domains such as manifolds and graphs. However, existing approaches cannot preserve all the desirable properties of Euclidean convolutions, namely compactly supported filters, directionality, transferability across different manifolds. In this paper we develop a new generalization of the convolution operation, referred to as parallel transport convolution (PTC), on Riemannian manifolds and their discrete counterparts. PTC is designed based on the parallel transportation which is able to translate information along a manifold and to intrinsically preserve directionality. PTC allows for the construction of compactly supported filters and is also robust to manifold deformations. This enables us to preform wavelet-like operations and to define deep convolutional neural networks on curved domains.

Connected through hyperedge HE-1 (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"
    - "roto translation covariant convolutional networks for medical image analysis"
    - "multi directional geodesic neural networks via equivariant convolution"

Connected through hyperedge HE-2 (induced by source paper "gauge equivariant convolutional networks and the icosahedral cnn"):
  with members:
    - "spherical cnns on unstructured grids"
    - "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"
    - "a general theory of equivariant cnns on homogeneous spaces"

Connected through hyperedge HE-3 (induced by source paper "horizontal flows and manifold stochastics in geometric deep learning"):
  with members:
    - "gauge equivariant convolutional networks and the icosahedral cnn"
    - "covariance in physics and convolutional neural networks"
    - "multi directional geodesic neural networks via equivariant convolution"
    - "the vector heat method"
    - "differential geometry and stochastic dynamics with deep learning numerics"
    - "geometric deep learning going beyond euclidean data"
    - "learning shape correspondence with anisotropic convolutional neural networks"
    - "geometric deep learning on graphs and manifolds using mixture model cnns"

Connected through hyperedge HE-4 (induced by source paper "zernet convolutional neural networks on arbitrary surfaces via zernike local tangent space estimation"):
  with members:
    - "gauge equivariant convolutional networks and the icosahedral cnn"
    - "meshcnn a network with an edge"
    - "multi directional geodesic neural networks via equivariant convolution"
    - "dynamic graph cnn for learning on point clouds"
    - "building statistical shape spaces for 3d human modeling"
    - "feastnet feature steered graph convolutions for 3d shape analysis"
    - "deep spectral descriptors learning the point wise correspondence metric via siamese deep neural networks"
    - "product manifold filter non rigid shape correspondence via kernel density estimation in the product space"

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)