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 neural networks exponentially lose expressive power for node classification"
  Abstract: Graph Neural Networks (graph NNs) are a promising deep learning approach for analyzing graph-structured data. However, it is known that they do not improve (or sometimes worsen) their predictive performance as we pile up many layers and add non-lineality. To tackle this problem, we investigate the expressive power of graph NNs via their asymptotic behaviors as the layer size tends to infinity. Our strategy is to generalize the forward propagation of a Graph Convolutional Network (GCN), which is a popular graph NN variant, as a specific dynamical system. In the case of a GCN, we show that when its weights satisfy the conditions determined by the spectra of the (augmented) normalized Laplacian, its output exponentially approaches the set of signals that carry information of the connected components and node degrees only for distinguishing nodes. Our theory enables us to relate the expressive power of GCNs with the topological information of the underlying graphs inherent in the graph spectra. To demonstrate this, we characterize the asymptotic behavior of GCNs on the Erd\H{o}s -- R\'{e}nyi graph. We show that when the Erd\H{o}s -- R\'{e}nyi graph is sufficiently dense and large, a broad range of GCNs on it suffers from the "information loss" in the limit of infinite layers with high probability. Based on the theory, we provide a principled guideline for weight normalization of graph NNs. We experimentally confirm that the proposed weight scaling enhances the predictive performance of GCNs in real data. Code is available at https://github.com/delta2323/gnn-asymptotics.

Connected through hyperedge HE-1 (induced by source paper "self enhanced gnn improving graph neural networks using model outputs"):
  with members:
    - "geom gcn geometric graph convolutional networks"
    - "measuring and relieving the over smoothing problem for graph neural networks from the topological view"
    - "unifying graph convolutional neural networks and label propagation"
    - "fast graph representation learning with pytorch geometric"
    - "understanding and utilizing deep neural networks trained with noisy labels"
    - "graph neural networks a review of methods and applications"
    - "pitfalls of graph neural network evaluation"
    - "representation learning on graphs with jumping knowledge networks"

Connected through hyperedge HE-2 (induced by source paper "graph neural ordinary differential equations"):
  with members:
    - "universal differential equations for scientific machine learning"
    - "exact combinatorial optimization with graph convolutional neural networks"
    - "latent odes for irregularly sampled time series"
    - "anode unconditionally accurate memory efficient gradients for neural odes"
    - "hamiltonian graph networks with ode integrators"
    - "gresnet graph residual network for reviving deep gnns from suspended animation"
    - "fast and deep graph neural networks"
    - "a differentiable programming system to bridge machine learning and scientific computing"

Connected through hyperedge HE-3 (induced by source paper "continuous graph neural networks"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "gmnn graph markov neural networks"
    - "break the ceiling stronger multi scale deep graph convolutional networks"
    - "diffusion improves graph learning"
    - "understanding the representation power of graph neural networks in learning graph topology"
    - "discrete and continuous deep residual learning over graphs"
    - "latent odes for irregularly sampled time series"
    - "simplifying graph convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "an anatomy of graph neural networks going deep via the lens of mutual information exponential decay vs full preservation"):
  with members:
    - "on asymptotic behaviors of graph cnns from dynamical systems perspective"
    - "simplifying graph convolutional networks"
    - "a comprehensive survey on graph neural networks"
    - "deepgcns can gcns go as deep as cnns"
    - "graph neural networks a review of methods and applications"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "graph to sequence learning using gated graph neural networks"
    - "semi supervised user geolocation via graph convolutional networks"

Connected through hyperedge HE-5 (induced by source paper "on the choice of graph neural network architectures"):
  with members:
    - "simplifying graph convolutional networks"
    - "fast graph representation learning with pytorch geometric"
    - "structured agents for physical construction"
    - "a comprehensive survey on graph neural networks"
    - "predict then propagate graph neural networks meet personalized pagerank"
    - "dual primal graph convolutional networks"
    - "pitfalls of graph neural network evaluation"
    - "dynamic graph cnn for learning on point clouds"

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)