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 "improving node classification by co training node pair classification a novel training framework for general graph neural networks"):
  Abstract: Semi-supervised learning is a widely used training framework for graph node classification. However, there are two problems existing in this learning method: (1) the original graph topology may not be perfectly aligned with the node classification task; (2) the supervision information in the training set has not been fully used. To tackle these two problems, we design a new task: node pair classification, to assist in training GNN models for the target node classification task. We further propose a novel training framework named Adaptive Co-training, which jointly trains the node classification and the node pair classification after the optimization of graph topology. Extensive experimental results on four representative GNN models have demonstrated that our proposed training framework significantly outperforms baseline methods across three benchmark graph datasets.

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 "measuring and relieving the over smoothing problem for graph neural networks from the topological view":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "spectral graph attention network")
  Member "evolvegcn evolving graph convolutional networks for dynamic graphs":
    also connected through other hyperedges:
      - Hyperedge HE-3 (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-4 (induced by source paper "bridging the gap between spatial and spectral domains a survey on graph neural networks")
  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 "measuring and relieving the over smoothing problem for graph neural networks from the topological view")
  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)