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: "self attention graph pooling"
  Abstract: Advanced methods of applying deep learning to structured data such as graphs have been proposed in recent years. In particular, studies have focused on generalizing convolutional neural networks to graph data, which includes redefining the convolution and the downsampling (pooling) operations for graphs. The method of generalizing the convolution operation to graphs has been proven to improve performance and is widely used. However, the method of applying downsampling to graphs is still difficult to perform and has room for improvement. In this paper, we propose a graph pooling method based on self-attention. Self-attention using graph convolution allows our pooling method to consider both node features and graph topology. To ensure a fair comparison, the same training procedures and model architectures were used for the existing pooling methods and our method. The experimental results demonstrate that our method achieves superior graph classification performance on the benchmark datasets using a reasonable number of parameters.

Connected through hyperedge HE-1 (induced by source paper "memory based graph networks"):
  with members:
    - "mincut pooling in graph neural networks"
    - "clique pooling for graph classification"
    - "graph u nets"
    - "generating diverse high fidelity images with vq vae 2"
    - "relational graph representation learning for open domain question answering"
    - "unsupervised multi task feature learning on point clouds"
    - "large memory layers with product keys"
    - "a comprehensive survey on graph neural networks"

Connected through hyperedge HE-2 (induced by source paper "benchmarking graph neural networks"):
  with members:
    - "understanding attention and generalization in graph neural networks"
    - "a fair comparison of graph neural networks for graph classification"
    - "are powerful graph neural nets necessary a dissection on graph classification"
    - "a two step graph convolutional decoder for molecule generation"
    - "on graph classification networks datasets and baselines"
    - "gnn film graph neural networks with feature wise linear modulation"
    - "an efficient graph convolutional network technique for the travelling salesman problem"
    - "a gentle introduction to deep learning for graphs"

Connected through hyperedge HE-3 (induced by source paper "a comprehensive study on the prediction reliability of graph neural networks for virtual screening"):
  with members:
    - "bayesian deep learning and a probabilistic perspective of generalization"
    - "when does label smoothing help"
    - "using self supervised learning can improve model robustness and uncertainty"
    - "data efficient image recognition with contrastive predictive coding"
    - "pre training graph neural networks"
    - "dropconnect is effective in modeling uncertainty of bayesian deep networks"
    - "bayesian semi supervised learning for uncertainty calibrated prediction of molecular properties and active learning"
    - "using pre training can improve model robustness and uncertainty"

Connected through hyperedge HE-4 (induced by source paper "few shot learning on graphs via super classes based on graph spectral measures"):
  with members:
    - "a survey on graph kernels"
    - "a closer look at few shot classification"
    - "a comprehensive survey on graph neural networks"
    - "on first order meta learning algorithms"
    - "meta learning with latent embedding optimization"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "relational inductive biases deep learning and graph networks"
    - "how powerful are graph neural networks"

Connected through hyperedge HE-5 (induced by source paper "deep graph mapper seeing graphs through the neural lens"):
  with members:
    - "mincut pooling in graph neural networks"
    - "asap adaptive structure aware pooling for learning hierarchical graph representations"
    - "graph convolutional networks with eigenpooling"
    - "clique pooling for graph classification"
    - "graph u nets"
    - "on graph classification networks datasets and baselines"
    - "graph filtration learning"
    - "gnnexplainer generating explanations for graph neural networks"

Connected through hyperedge HE-6 (induced by source paper "few shot graph classification with model agnostic meta learning"):
  with members:
    - "hierarchical graph pooling with structure learning"
    - "understanding attention and generalization in graph neural networks"
    - "learning to propagate for graph meta learning"
    - "graph u nets"
    - "edge labeling graph neural network for few shot learning"
    - "edge contraction pooling for graph neural networks"
    - "meta learning with latent embedding optimization"
    - "how to train your maml"

Connected through hyperedge HE-7 (induced by source paper "a dynamic reduction network for point clouds"):
  with members:
    - "fast graph representation learning with pytorch geometric"
    - "edge contraction pooling for graph neural networks"
    - "dynamic graph cnn for learning on point clouds"
    - "decoupled weight decay regularization"
    - "neural message passing for quantum chemistry"
    - "geometric deep learning going beyond euclidean data"
    - "geometric deep learning on graphs and manifolds using mixture model cnns"

Connected through hyperedge HE-8 (induced by source paper "structure feature based graph self adaptive pooling"):
  with members:
    - "graph convolutional networks with eigenpooling"
    - "graph u nets"
    - "fast graph representation learning with pytorch geometric"
    - "semi supervised classification with graph convolutional networks"
    - "deep residual learning for image recognition"
    - "spectral networks and locally connected networks on graphs"

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)