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: "the truly deep graph convolutional networks for node classification"
  Abstract: Existing Graph Convolutional Networks (GCNs) are shallow---the number of the layers is usually not larger than 2. The deeper variants by simply stacking more layers, unfortunately perform worse, even involving well-known tricks like weight penalizing, dropout, and residual connections. This paper reveals that developing deep GCNs mainly encounters two obstacles: \emph{over-fitting} and \emph{over-smoothing}. The over-fitting issue weakens the generalization ability on small graphs, while over-smoothing impedes model training by isolating output representations from the input features with the increase in network depth. Hence, we propose DropEdge, a novel technique to alleviate both issues. At its core, DropEdge randomly removes a certain number of edges from the input graphs, acting like a data augmenter and also a message passing reducer. More importantly, DropEdge enables us to recast a wider range of Convolutional Neural Networks (CNNs) from the image field to the graph domain; in particular, we study DenseNet and InceptionNet in this paper. Extensive experiments on several benchmarks demonstrate that our method allows deep GCNs to achieve promising performance, even when the number of layers exceeds 30---the deepest GCN that has ever been proposed.

Connected through hyperedge HE-1 (induced by source paper "ripple walk training a subgraph based training framework for large and deep graph neural network"):
  with members:
    - "lanczosnet multi scale deep graph convolutional networks"
    - "pairnorm tackling oversmoothing in gnns"
    - "graphsaint graph sampling based inductive learning method"
    - "mixhop higher order graph convolutional architectures via sparsified neighborhood mixing"
    - "cluster gcn an efficient algorithm for training deep and large graph convolutional networks"
    - "gresnet graph residual network for reviving deep gnns from suspended animation"
    - "graph star net for generalized multi task learning"
    - "graph convolutional neural networks for web scale recommender systems"

Connected through hyperedge HE-2 (induced by source paper "rumor detection on social media with bi directional graph convolutional networks"):
  with members:
    - "detection and resolution of rumours in social media a survey"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "spectral networks and locally connected networks on graphs"

Connected through hyperedge HE-3 (induced by source paper "measuring and relieving the over smoothing problem for graph neural networks from the topological view"):
  with members:
    - "evolvegcn evolving graph convolutional networks for dynamic graphs"
    - "graph neural networks with convolutional arma filters"
    - "fisher bures adversary graph convolutional networks"
    - "coherent comment generation for chinese articles with a graph to sequence model"
    - "fast graph representation learning with pytorch geometric"
    - "hypergraph convolution and hypergraph attention"
    - "just jump dynamic neighborhood aggregation in graph neural networks"
    - "deepgcns can gcns go as deep as cnns"

Connected through hyperedge HE-4 (induced by source paper "improving node classification by co training node pair classification a novel training framework for general graph neural networks"):
  with members:
    - "measuring and relieving the over smoothing problem for graph neural networks from the topological view"
    - "evolvegcn evolving graph convolutional networks for dynamic graphs"
    - "graph neural networks with convolutional arma filters"
    - "coherent comment generation for chinese articles with a graph to sequence model"
    - "fast graph representation learning with pytorch geometric"
    - "hypergraph convolution and hypergraph attention"
    - "just jump dynamic neighborhood aggregation in graph neural networks"
    - "demo net degree specific graph neural networks for node and graph classification"

Connected through hyperedge HE-5 (induced by source paper "pairnorm tackling oversmoothing in gnns"):
  with members:
    - "gmnn graph markov neural networks"
    - "simplifying graph convolutional networks"
    - "just jump dynamic neighborhood aggregation in graph neural networks"
    - "pitfalls of graph neural network evaluation"
    - "representation learning on graphs with jumping knowledge networks"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "semi supervised classification with graph convolutional networks"
    - "weight normalization a simple reparameterization to accelerate training of deep 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)