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: "stability and generalization of graph convolutional neural networks"
  Abstract: Inspired by convolutional neural networks on 1D and 2D data, graph convolutional neural networks (GCNNs) have been developed for various learning tasks on graph data, and have shown superior performance on real-world datasets. Despite their success, there is a dearth of theoretical explorations of GCNN models such as their generalization properties. In this paper, we take a first step towards developing a deeper theoretical understanding of GCNN models by analyzing the stability of single-layer GCNN models and deriving their generalization guarantees in a semi-supervised graph learning setting. In particular, we show that the algorithmic stability of a GCNN model depends upon the largest absolute eigenvalue of its graph convolution filter. Moreover, to ensure the uniform stability needed to provide strong generalization guarantees, the largest absolute eigenvalue must be independent of the graph size. Our results shed new insights on the design of new & improved graph convolution filters with guaranteed algorithmic stability. We evaluate the generalization gap and stability on various real-world graph datasets and show that the empirical results indeed support our theoretical findings. To the best of our knowledge, we are the first to study stability bounds on graph learning in a semi-supervised setting and derive generalization bounds for GCNN models.

Connected through hyperedge HE-1 (induced by source paper "generalization and representational limits of graph neural networks"):
  with members:
    - "directional message passing for molecular graphs"
    - "efficient probabilistic logic reasoning with graph neural networks"
    - "universal invariant and equivariant graph neural networks"
    - "graph transformer networks"
    - "understanding the representation power of graph neural networks in learning graph topology"
    - "on the universality of invariant networks"
    - "approximation ratios of graph neural networks for combinatorial problems"
    - "a survey on graph kernels"

Connected through hyperedge HE-2 (induced by source paper "learning universal graph neural network embeddings with aid of transfer learning"):
  with members:
    - "graph convolutional policy network for goal directed molecular graph generation"
    - "adaptive graph convolutional neural networks"
    - "the natural language decathlon multitask learning as question answering"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "hierarchical graph representation learning with differentiable pooling"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "deep contextualized word representations"

Connected through hyperedge HE-3 (induced by source paper "graph neural networks exponentially lose expressive power for node classification"):
  with members:
    - "optuna a next generation hyperparameter optimization framework"
    - "break the ceiling stronger multi scale deep graph convolutional networks"
    - "on the universality of invariant networks"
    - "chainer a deep learning framework for accelerating the research cycle"
    - "simplifying graph convolutional networks"
    - "a comprehensive survey on graph neural networks"
    - "deepgcns can gcns go as deep as cnns"
    - "graph r cnn for scene graph generation"

Connected through hyperedge HE-4 (induced by source paper "understanding the representation power of graph neural networks in learning graph topology"):
  with members:
    - "a comprehensive survey on graph neural networks"
    - "graph neural tangent kernel fusing graph neural networks with graph kernels"
    - "relational pooling for graph representations"
    - "predicting graph categories from structural properties"
    - "graph convolutional policy network for goal directed molecular graph generation"
    - "weisfeiler and leman go neural higher order graph neural networks"
    - "representation learning on graphs with jumping knowledge networks"
    - "deep learning on graphs a survey"

Connected through hyperedge HE-5 (induced by source paper "understanding isomorphism bias in graph data sets"):
  with members:
    - "semantic redundancies in image classification datasets the 10 you don t need"
    - "do we train on test data purging cifar of near duplicates"
    - "a survey on graph kernels"
    - "gnn explainer a tool for post hoc explanation of graph neural networks"
    - "do imagenet classifiers generalize to imagenet"
    - "fast graph representation learning with pytorch geometric"
    - "provably powerful graph networks"
    - "a comprehensive survey on graph neural networks"

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)