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: "graph classification with 2d convolutional neural networks"
  Abstract: Graph learning is currently dominated by graph kernels, which, while powerful, suffer some significant limitations. Convolutional Neural Networks (CNNs) offer a very appealing alternative, but processing graphs with CNNs is not trivial. To address this challenge, many sophisticated extensions of CNNs have recently been introduced. In this paper, we reverse the problem: rather than proposing yet another graph CNN model, we introduce a novel way to represent graphs as multi-channel image-like structures that allows them to be handled by vanilla 2D CNNs. Experiments reveal that our method is more accurate than state-of-the-art graph kernels and graph CNNs on 4 out of 6 real-world datasets (with and without continuous node attributes), and close elsewhere. Our approach is also preferable to graph kernels in terms of time complexity. Code and data are publicly available.

Connected through hyperedge HE-1 (induced by source paper "deep graph similarity learning a survey"):
  with members:
    - "simgnn a neural network approach to fast graph similarity computation"
    - "deep graph matching consensus"
    - "dynamic graph convolutional networks"
    - "glmnet graph learning matching networks for feature matching"
    - "from community to role based graph embeddings"
    - "learning combinatorial embedding networks for deep graph matching"
    - "community preserving graph convolutions for structural and functional joint embedding of brain networks"
    - "generalizing from a few examples a survey on few shot learning"

Connected through hyperedge HE-2 (induced by source paper "ddgk learning graph representations for deep divergence graph kernels"):
  with members:
    - "is a single embedding enough learning node representations that capture multiple social contexts"
    - "netlsd hearing the shape of a graph"
    - "hierarchical graph representation learning with differentiable pooling"
    - "weisfeiler and leman go neural higher order graph neural networks"
    - "a tutorial on network embeddings"
    - "graph edit distance computation via graph neural networks"
    - "verse versatile graph embeddings from similarity measures"
    - "relational inductive biases deep learning and graph networks"

Connected through hyperedge HE-3 (induced by source paper "graph preserving grid layout a simple graph drawing method for graph classification using cnns"):
  with members:
    - "a deep generative model for graph layout"
    - "semi supervised graph classification a hierarchical graph perspective"
    - "graph u nets"
    - "simplifying graph convolutional networks"
    - "learning discrete structures for graph neural networks"
    - "graph star net for generalized multi task learning"
    - "a comprehensive survey on graph neural networks"
    - "weisfeiler and leman go neural higher order graph neural networks"

Connected through hyperedge HE-4 (induced by source paper "stability and generalization of graph convolutional neural networks"):
  with members:
    - "adaptive graph convolutional neural networks"
    - "hierarchical graph representation learning with differentiable pooling"
    - "how powerful are graph neural networks"
    - "exploring graph structured passage representation for multi hop reading comprehension with graph neural networks"
    - "covariant compositional networks for learning graphs"
    - "unifying local and non local signal processing with graph cnns"
    - "deriving neural architectures from sequence and graph kernels"
    - "exploring generalization in deep learning"

Connected through hyperedge HE-5 (induced by source paper "message passing attention networks for document understanding"):
  with members:
    - "dialogue act classification with context aware self attention"
    - "energy based self attentive learning of abstractive communities for spoken language understanding"
    - "representation learning on graphs with jumping knowledge networks"
    - "graph convolutional networks for text classification"
    - "how powerful are graph neural networks"
    - "kernel graph convolutional neural networks"
    - "neural message passing for quantum chemistry"
    - "a structured self attentive sentence embedding"

Connected through hyperedge HE-6 (induced by source paper "graph analysis and graph pooling in the spatial domain"):
  with members:
    - "graph u nets"
    - "self attention graph pooling"
    - "how powerful are graph neural networks"
    - "splinecnn fast geometric deep learning with continuous b spline kernels"
    - "spatial random sampling a structure preserving data sketching tool"
    - "dynamic edge conditioned filters in convolutional neural networks on graphs"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "representation learning on graphs methods and applications"

Connected through hyperedge HE-7 (induced by source paper "graph hist graph classification from latent feature histograms with application to bot detection"):
  with members:
    - "fast graph representation learning with pytorch geometric"
    - "a comprehensive survey on graph neural networks"
    - "hierarchical graph representation learning with differentiable pooling"
    - "deep neural networks for bot detection"
    - "anonymous walk embeddings"
    - "botornot a system to evaluate social bots"
    - "semi supervised classification with graph convolutional networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"

Connected through hyperedge HE-8 (induced by source paper "multi resolution graph neural network for identifying disease specific variations in brain connectivity"):
  with members:
    - "sdrsac semidefinite based randomized approach for robust point cloud registration without correspondences"
    - "detailed human shape estimation from a single image by hierarchical mesh deformation"
    - "dynamic graph cnn for learning on point clouds"
    - "dynamic edge conditioned filters in convolutional neural networks on graphs"
    - "graph2vec learning distributed representations of graphs"
    - "semi supervised classification with graph convolutional networks"
    - "learning convolutional neural networks for graphs"
    - "convolutional neural networks on graphs with fast localized spectral filtering"

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)