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: "splinecnn fast geometric deep learning with continuous b spline kernels"
  Abstract: We present Spline-based Convolutional Neural Networks (SplineCNNs), a variant of deep neural networks for irregular structured and geometric input, e.g., graphs or meshes. Our main contribution is a novel convolution operator based on B-splines, that makes the computation time independent from the kernel size due to the local support property of the B-spline basis functions. As a result, we obtain a generalization of the traditional CNN convolution operator by using continuous kernel functions parametrized by a fixed number of trainable weights. In contrast to related approaches that filter in the spectral domain, the proposed method aggregates features purely in the spatial domain. In addition, SplineCNN allows entire end-to-end training of deep architectures, using only the geometric structure as input, instead of handcrafted feature descriptors. For validation, we apply our method on tasks from the fields of image graph classification, shape correspondence and graph node classification, and show that it outperforms or pars state-of-the-art approaches while being significantly faster and having favorable properties like domain-independence.

Connected through hyperedge HE-1 (induced by source paper "deep graph matching consensus"):
  with members:
    - "simgnn a neural network approach to fast graph similarity computation"
    - "learning combinatorial embedding networks for deep graph matching"
    - "cross lingual knowledge graph alignment via graph matching neural network"
    - "computing optimal assignments in linear time for approximate graph matching"
    - "scalable gromov wasserstein learning for graph partitioning and matching"
    - "deep adversarial network alignment"
    - "graph neural networks for user identity linkage"
    - "kergm kernelized graph matching"

Connected through hyperedge HE-2 (induced by source paper "deep iterative surface normal estimation"):
  with members:
    - "fast graph representation learning with pytorch geometric"
    - "deep closest point learning representations for point cloud registration"
    - "learning transformation synchronization"
    - "ppfnet global context aware local features for robust 3d point matching"
    - "discovery of latent 3d keypoints via end to end geometric reasoning"
    - "weisfeiler and leman go neural higher order graph neural networks"
    - "ppf foldnet unsupervised learning of rotation invariant 3d local descriptors"
    - "nesti net normal estimation for unstructured 3d point clouds using convolutional neural networks"

Connected through hyperedge HE-3 (induced by source paper "adversarial generation of continuous implicit shape representations"):
  with members:
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "deep meta functionals for shape representation"
    - "meshcnn a network with an edge"
    - "sdm net deep generative network for structured deformable mesh"
    - "learning implicit fields for generative shape modeling"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "a survey on deep learning advances on different 3d data representations"
    - "occupancy networks learning 3d reconstruction in function space"

Connected through hyperedge HE-4 (induced by source paper "mgcn descriptor learning using multiscale gcns"):
  with members:
    - "graph wavelet neural network"
    - "ppfnet global context aware local features for robust 3d point matching"
    - "dynamic graph cnn for learning on point clouds"
    - "learning compact geometric features"
    - "geometric deep learning going beyond euclidean data"
    - "working hard to know your neighbor s margins local descriptor learning loss"
    - "semi supervised classification with graph convolutional networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"

Connected through hyperedge HE-5 (induced by source paper "superpixel image classification with graph attention networks"):
  with members:
    - "learning attention based embeddings for relation prediction in knowledge graphs"
    - "geometric graph convolutional neural networks"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "geometric deep learning on graphs and manifolds using mixture model cnns"

Connected through hyperedge HE-6 (induced by source paper "human action recognition with multi laplacian graph convolutional networks"):
  with members:
    - "graph neural networks with convolutional arma filters"
    - "monte carlo convolution for learning on non uniformly sampled point clouds"
    - "simplifying graph convolutional networks"
    - "graph wavelet neural network"
    - "graph convolutional neural networks via scattering"
    - "a comprehensive survey on graph neural networks"
    - "part based graph convolutional network for action recognition"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"

Connected through hyperedge HE-7 (induced by source paper "meshgan non linear 3d morphable models of faces"):
  with members:
    - "generating 3d faces using convolutional mesh autoencoders"
    - "inverting the generator of a generative adversarial network ii"
    - "relational inductive biases deep learning and graph networks"
    - "escaping from collapsing modes in a constrained space"
    - "a simple approach to intrinsic correspondence learning on unstructured 3d meshes"
    - "motifnet a motif based graph convolutional network for directed graphs"
    - "nonlinear 3d face morphable model"
    - "variational autoencoders for deforming 3d mesh models"

Connected through hyperedge HE-8 (induced by source paper "graph based spatial temporal feature learning for neuromorphic vision sensing"):
  with members:
    - "spatiotemporal filtering for event based action recognition"
    - "graph based object classification for neuromorphic vision sensing"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "attention mechanisms for object recognition with event based cameras"
    - "local spectral graph convolution for point set feature learning"
    - "how powerful are graph neural networks"
    - "hats histograms of averaged time surfaces for robust event based object classification"
    - "ev flownet self supervised optical flow estimation for event based cameras"

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)