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: "graph convolutional networks with eigenpooling"
  Abstract: Graph neural networks, which generalize deep neural network models to graph structured data, have attracted increasing attention in recent years. They usually learn node representations by transforming, propagating and aggregating node features and have been proven to improve the performance of many graph related tasks such as node classification and link prediction. To apply graph neural networks for the graph classification task, approaches to generate the \textit{graph representation} from node representations are demanded. A common way is to globally combine the node representations. However, rich structural information is overlooked. Thus a hierarchical pooling procedure is desired to preserve the graph structure during the graph representation learning. There are some recent works on hierarchically learning graph representation analogous to the pooling step in conventional convolutional neural (CNN) networks. However, the local structural information is still largely neglected during the pooling process. In this paper, we introduce a pooling operator $\pooling$ based on graph Fourier transform, which can utilize the node features and local structures during the pooling process. We then design pooling layers based on the pooling operator, which are further combined with traditional GCN convolutional layers to form a graph neural network framework $\m$ for graph classification. Theoretical analysis is provided to understand $\pooling$ from both local and global perspectives. Experimental results of the graph classification task on $6$ commonly used benchmarks demonstrate the effectiveness of the proposed framework.

Connected through hyperedge HE-1 (induced by source paper "transferring robustness for graph neural network against poisoning attacks"):
  with members:
    - "adversarial examples on graph data deep insights into attack and defense"
    - "power up robust graph convolutional network against evasion attacks based on graph powering"
    - "topology attack and defense for graph neural networks an optimization perspective"
    - "graph neural networks with generated parameters for relation extraction"
    - "graph few shot learning via knowledge transfer"
    - "graph neural networks with high order feature interactions"
    - "regional homogeneity towards learning transferable universal adversarial perturbations against defenses"
    - "learning from multiple cities a meta learning approach for spatial temporal prediction"

Connected through hyperedge HE-2 (induced by source paper "random features strengthen graph neural networks"):
  with members:
    - "universal invariant and equivariant graph neural networks"
    - "exact k recommendation via maximal clique optimization"
    - "kgat knowledge graph attention network for recommendation"
    - "graph u nets"
    - "on the universality of invariant networks"
    - "approximation ratios of graph neural networks for combinatorial problems"
    - "what graph neural networks cannot learn depth vs width"
    - "provably powerful graph networks"

Connected through hyperedge HE-3 (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"
    - "clique pooling for graph classification"
    - "graph u nets"
    - "on graph classification networks datasets and baselines"
    - "self attention graph pooling"
    - "graph filtration learning"
    - "gnnexplainer generating explanations for graph neural networks"

Connected through hyperedge HE-4 (induced by source paper "structure feature based graph self adaptive pooling"):
  with members:
    - "graph u nets"
    - "self attention graph pooling"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "towards interpretable sparse graph representation learning with laplacian pooling"):
  with members:
    - "a comprehensive survey on graph neural networks"
    - "graph convolutional policy network for goal directed molecular graph generation"
    - "predict then propagate graph neural networks meet personalized pagerank"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "hierarchical graph representation learning with differentiable pooling"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "learning deep generative models of graphs"

Connected through hyperedge HE-6 (induced by source paper "asap adaptive structure aware pooling for learning hierarchical graph representations"):
  with members:
    - "graph u nets"
    - "dating documents using graph convolution networks"
    - "fast graph representation learning with pytorch geometric"
    - "composition based multi relational graph convolutional networks"
    - "hierarchical graph representation learning with differentiable pooling"
    - "weisfeiler and leman go neural higher order graph neural networks"
    - "mt cgcnn integrating crystal graph convolutional neural network with multitask learning for material property prediction"
    - "reside improving distantly supervised neural relation extraction using side information"

Connected through hyperedge HE-7 (induced by source paper "hierarchical graph pooling with structure learning"):
  with members:
    - "learning graph pooling and hybrid convolutional operations for text representations"
    - "graph u nets"
    - "self attention graph pooling"
    - "ipool information based pooling in hierarchical graph neural networks"
    - "edge contraction pooling for graph neural networks"
    - "graph neural networks a review of methods and applications"
    - "graph convolutional neural networks for web scale recommender systems"
    - "adaptive graph convolutional neural networks"

Connected through hyperedge HE-8 (induced by source paper "haar graph pooling"):
  with members:
    - "pan path integral based convolution for deep graph neural networks"
    - "fast haar transforms for graph neural networks"
    - "understanding attention and generalization in graph neural networks"
    - "towards interpretable sparse graph representation learning with laplacian pooling"
    - "self attention graph pooling"
    - "fast graph representation learning with pytorch geometric"
    - "a comprehensive survey on graph neural networks"
    - "graph neural networks a review of methods and applications"

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)