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: "on asymptotic behaviors of graph cnns from dynamical systems perspective"
  Abstract: Graph Convolutional Neural Networks (graph CNNs) 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 more layers and make them deeper. To tackle this problem, we investigate the expressive power of graph CNNs by analyzing 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 one of the most popular graph CNN variants, as a specific dynamical system. In the case of GCNs, we show that when the weights satisfy the conditions determined by the spectra of the (augmented) normalized Laplacian, the output of GCNs exponentially approaches the set of signals that carry only information of the connected components and node degrees for distinguishing nodes. Our theory enables us to directly relate the expressive power of GCNs with the topological information of the underlying graphs, which is inherent in the graph spectra. To demonstrate this, we characterize the asymptotic behavior of GCNs on the Erdős -- Renyi graph. We show that when the Erdős -- Renyi graph is sufficiently dense and large, a wide range of GCNs on them suffers from this ``information loss" in the limit of infinite layers with high probability. Furthermore, our theory provides principled guidelines for the weight normalization of graph CNNs. We experimentally confirmed that weight scaling based on our theory enhanced the predictive performance of GCNs in real data.

Connected through hyperedge HE-1 (induced by source paper "dropedge towards deep graph convolutional networks on node classification"):
  with members:
    - "deep graph library towards efficient and scalable deep learning on graphs"
    - "a comprehensive survey on graph neural networks"
    - "deepgcns can gcns go as deep as cnns"
    - "adaptive graph convolutional neural networks"
    - "predict then propagate graph neural networks meet personalized pagerank"
    - "representation learning on graphs with jumping knowledge networks"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "large scale learnable graph convolutional networks"

Connected through hyperedge HE-2 (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:
    - "graph neural networks exponentially lose expressive power for node classification"
    - "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-3 (induced by source paper "graph residual flow for molecular graph generation"):
  with members:
    - "graph normalizing flows"
    - "mintnet building invertible neural networks with masked convolutions"
    - "simplifying graph convolutional networks"
    - "graph convolutional policy network for goal directed molecular graph generation"
    - "constrained graph variational autoencoders for molecule design"
    - "constrained generation of semantically valid graphs via regularizing variational autoencoders"
    - "spectral normalization for generative adversarial networks"
    - "automatic chemical design using a data driven continuous representation of molecules"

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)