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: "measuring and relieving the over smoothing problem for graph neural networks from the topological view"
  Abstract: Graph Neural Networks (GNNs) have achieved promising performance on a wide range of graph-based tasks. Despite their success, one severe limitation of GNNs is the over-smoothing issue (indistinguishable representations of nodes in different classes). In this work, we present a systematic and quantitative study on the over-smoothing issue of GNNs. First, we introduce two quantitative metrics, MAD and MADGap, to measure the smoothness and over-smoothness of the graph nodes representations, respectively. Then, we verify that smoothing is the nature of GNNs and the critical factor leading to over-smoothness is the low information-to-noise ratio of the message received by the nodes, which is partially determined by the graph topology. Finally, we propose two methods to alleviate the over-smoothing issue from the topological view: (1) MADReg which adds a MADGap-based regularizer to the training objective;(2) AdaGraph which optimizes the graph topology based on the model predictions. Extensive experiments on 7 widely-used graph datasets with 10 typical GNN models show that the two proposed methods are effective for relieving the over-smoothing issue, thus improving the performance of various GNN models.

Connected through hyperedge HE-1 (induced by source paper "spectral graph attention network"):
  with members:
    - "power up robust graph convolutional network against evasion attacks based on graph powering"
    - "stabilizing gans with octave convolutions"
    - "a restricted black box adversarial framework towards attacking graph embedding models"
    - "lanczosnet multi scale deep graph convolutional networks"
    - "graph neural networks with convolutional arma filters"
    - "dual graph attention networks for deep latent representation of multifaceted social effects in recommender systems"
    - "semi supervised graph classification a hierarchical graph perspective"
    - "graph wavelet neural network"

Connected through hyperedge HE-2 (induced by source paper "permutohedral gcn graph convolutional networks with global attention"):
  with members:
    - "reformer the efficient transformer"
    - "geom gcn geometric graph convolutional networks"
    - "permutohedral attention module for efficient non local neural networks"
    - "learning task specific generalized convolutions in the permutohedral lattice"
    - "graph u nets"
    - "position aware graph neural networks"
    - "hierarchical graph representation learning with differentiable pooling"
    - "representation learning on graphs with jumping knowledge networks"

Connected through hyperedge HE-3 (induced by source paper "self enhanced gnn improving graph neural networks using model outputs"):
  with members:
    - "geom gcn geometric graph convolutional networks"
    - "unifying graph convolutional neural networks and label propagation"
    - "graph neural networks exponentially lose expressive power for node classification"
    - "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-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:
    - "the truly deep graph convolutional networks for node classification"
    - "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"

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)