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: "generalizing the convolution operator to extend cnns to irregular domains"
  Abstract: Convolutional Neural Networks (CNNs) have become the state-of-the-art in supervised learning vision tasks. Their convolutional filters are of paramount importance for they allow to learn patterns while disregarding their locations in input images. When facing highly irregular domains, generalized convolutional operators based on an underlying graph structure have been proposed. However, these operators do not exactly match standard ones on grid graphs, and introduce unwanted additional invariance (e.g. with regards to rotations). We propose a novel approach to generalize CNNs to irregular domains using weight sharing and graph-based operators. Using experiments, we show that these models resemble CNNs on regular domains and offer better performance than multilayer perceptrons on distorded ones.

Connected through hyperedge HE-1 (induced by source paper "learning aligned spatial graph convolutional networks for graph classification"):
  with members:
    - "concept drift and anomaly detection in graph streams"
    - "anonymous walk embeddings"
    - "dynamic edge conditioned filters in convolutional neural networks on graphs"
    - "semi supervised classification with graph convolutional networks"
    - "learning convolutional neural networks for graphs"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "deep convolutional networks on graph structured data"
    - "convolutional networks on graphs for learning molecular fingerprints"

Connected through hyperedge HE-2 (induced by source paper "learning vertex convolutional networks for graph classification"):
  with members:
    - "concept drift and anomaly detection in graph streams"
    - "learning convolutional neural networks for graphs"
    - "deep convolutional networks on graph structured data"
    - "convolutional networks on graphs for learning molecular fingerprints"
    - "diffusion convolutional neural networks"
    - "show and tell a neural image caption generator"
    - "spectral networks and locally connected networks on graphs"
    - "learning by transduction"

Connected through hyperedge HE-3 (induced by source paper "learning backtrackless aligned spatial graph convolutional networks for graph classification"):
  with members:
    - "concept drift and anomaly detection in graph streams"
    - "anonymous walk embeddings"
    - "learning convolutional neural networks for graphs"
    - "deep convolutional networks on graph structured data"
    - "diffusion convolutional neural networks"
    - "spectral networks and locally connected networks on graphs"
    - "learning by transduction"

Connected through hyperedge HE-4 (induced by source paper "end to end learning of energy based representations for irregularly sampled signals and images"):
  with members:
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "on learning optimized reaction diffusion processes for effective image restoration"

Connected through hyperedge HE-5 (induced by source paper "matching convolutional neural networks without priors about data"):
  with members:
    - "topology adaptive graph convolutional networks"
    - "convolutional neural networks on irregular domains through approximate translations on inferred graphs"
    - "dynamic edge conditioned filters in convolutional neural networks on graphs"
    - "neural message passing for quantum chemistry"
    - "motif based convolutional neural network on graphs"
    - "geometric deep learning going beyond euclidean data"
    - "cayleynets graph convolutional neural networks with complex rational spectral filters"
    - "a neighborhood preserving translation operator on graphs"

Connected through hyperedge HE-6 (induced by source paper "graph convolutional neural networks based on quantum vertex saliency"):
  with members:
    - "tree based kernel for graphs with continuous attributes"
    - "concept drift and anomaly detection in graph streams"
    - "how powerful are graph neural networks"
    - "anonymous walk embeddings"
    - "dynamic edge conditioned filters in convolutional neural networks on graphs"
    - "semi supervised classification with graph convolutional networks"
    - "learning convolutional neural networks for graphs"
    - "convolutional neural networks on graphs with fast localized spectral filtering"

Connected through hyperedge HE-7 (induced by source paper "foldingnet point cloud auto encoder via deep grid deformation"):
  with members:
    - "deep sets"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"
    - "3d shape segmentation via shape fully convolutional networks"
    - "mining point cloud local structures by kernel correlation and graph pooling"
    - "dynamic edge conditioned filters in convolutional neural networks on graphs"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "geometric deep learning going beyond euclidean data"
    - "cayleynets graph convolutional neural networks with complex rational spectral filters"

Connected through hyperedge HE-8 (induced by source paper "graph classification with 2d convolutional neural networks"):
  with members:
    - "attributed social network embedding"
    - "a unifying view of explicit and implicit feature maps for structured data systematic studies of graph kernels"
    - "node2vec scalable feature learning for networks"
    - "densely connected convolutional networks"
    - "semi supervised classification with graph convolutional networks"
    - "learning convolutional neural networks for graphs"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "faster kernels for graphs with continuous attributes via hashing"

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)