human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "the truly deep graph convolutional networks for node classification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "ripple walk training a subgraph based training framework for large and deep graph neural network")
  Member "evolvegcn evolving graph convolutional networks for dynamic graphs":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "temporal attribute prediction via joint modeling of multi relational structure evolution")
  Member "graph neural networks with convolutional arma filters":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "bridging the gap between spatial and spectral domains a survey on graph neural networks")
  Member "fisher bures adversary graph convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "relation embedding for personalised poi recommendation")
  Member "coherent comment generation for chinese articles with a graph to sequence model":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "improving node classification by co training node pair classification a novel training framework for general graph neural networks")
  Member "fast graph representation learning with pytorch geometric":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on feature normalization and data augmentation")
  Member "hypergraph convolution and hypergraph attention":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "spectral graph attention network")
  Member "just jump dynamic neighborhood aggregation in graph neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "fast graph representation learning with pytorch geometric")

Question: Please tell me which class the source paper 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)