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: "multi stage self supervised learning for graph convolutional networks"
  Abstract: Graph Convolutional Networks(GCNs) play a crucial role in graph learning tasks, however, learning graph embedding with few supervised signals is still a difficult problem. In this paper, we propose a novel training algorithm for Graph Convolutional Network, called Multi-Stage Self-Supervised(M3S) Training Algorithm, combined with self-supervised learning approach, focusing on improving the generalization performance of GCNs on graphs with few labeled nodes. Firstly, a Multi-Stage Training Framework is provided as the basis of M3S training method. Then we leverage DeepCluster technique, a popular form of self-supervised learning, and design corresponding aligning mechanism on the embedding space to refine the Multi-Stage Training Framework, resulting in M3S Training Algorithm. Finally, extensive experimental results verify the superior performance of our algorithm on graphs with few labeled nodes under different label rates compared with other state-of-the-art approaches.

Connected through hyperedge HE-1 (induced by source paper "self supervised graph representation learning via global context prediction"):
  with members:
    - "self supervised dialogue learning"
    - "gmnn graph markov neural networks"
    - "self supervised visual feature learning with deep neural networks a survey"
    - "attributed graph clustering via adaptive graph convolution"
    - "graph wavelet neural network"
    - "grasp2vec learning object representations from self supervised grasping"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "adaptive graph convolutional neural networks"

Connected through hyperedge HE-2 (induced by source paper "break the ceiling stronger multi scale deep graph convolutional networks"):
  with members:
    - "lanczosnet multi scale deep graph convolutional networks"
    - "a comprehensive survey on graph neural networks"
    - "virtual adversarial training on graph convolutional networks in node classification"
    - "adaptive graph convolutional neural networks"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "graph partition neural networks for semi supervised classification"
    - "fastgcn fast learning with graph convolutional networks via importance sampling"
    - "neural message passing for quantum chemistry"

Connected through hyperedge HE-3 (induced by source paper "dynamic self training framework for graph convolutional networks"):
  with members:
    - "pitfalls of graph neural network evaluation"
    - "representation learning on graphs with jumping knowledge networks"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "fastgcn fast learning with graph convolutional networks via importance sampling"
    - "bootstrapped graph diffusions exposing the power of nonlinearity"
    - "predicting the effectiveness of self training application to sentiment classification"
    - "node2vec scalable feature learning for networks"
    - "geometric deep learning on graphs and manifolds using mixture model cnns"

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)