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: "understanding attention and generalization in graph neural networks"
  Abstract: We aim to better understand attention over nodes in graph neural networks (GNNs) and identify factors influencing its effectiveness. We particularly focus on the ability of attention GNNs to generalize to larger, more complex or noisy graphs. Motivated by insights from the work on Graph Isomorphism Networks, we design simple graph reasoning tasks that allow us to study attention in a controlled environment. We find that under typical conditions the effect of attention is negligible or even harmful, but under certain conditions it provides an exceptional gain in performance of more than 60% in some of our classification tasks. Satisfying these conditions in practice is challenging and often requires optimal initialization or supervised training of attention. We propose an alternative recipe and train attention in a weakly-supervised fashion that approaches the performance of supervised models, and, compared to unsupervised models, improves results on several synthetic as well as real datasets. Source code and datasets are available at this https URL.

Connected through hyperedge HE-1 (induced by source paper "a survey on the expressive power of graph neural networks"):
  with members:
    - "random features strengthen graph neural networks"
    - "generalization and representational limits of graph neural networks"
    - "universal invariant and equivariant graph neural networks"
    - "exact combinatorial optimization with graph convolutional neural networks"
    - "exact k recommendation via maximal clique optimization"
    - "constant time graph neural networks"
    - "kgat knowledge graph attention network for recommendation"
    - "on the universality of invariant networks"

Connected through hyperedge HE-2 (induced by source paper "benchmarking graph neural networks"):
  with members:
    - "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"
    - "self attention graph pooling"
    - "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 "few shot graph classification with model agnostic meta learning"):
  with members:
    - "hierarchical graph pooling with structure learning"
    - "learning to propagate for graph meta learning"
    - "graph u nets"
    - "edge labeling graph neural network for few shot learning"
    - "self attention graph pooling"
    - "edge contraction pooling for graph neural networks"
    - "meta learning with latent embedding optimization"
    - "how to train your maml"

Connected through hyperedge HE-4 (induced by source paper "hierarchical representation learning in graph neural networks with node decimation pooling"):
  with members:
    - "mincut pooling in graph neural networks"
    - "graph neural networks with convolutional arma filters"
    - "towards interpretable sparse graph representation learning with laplacian pooling"
    - "clique pooling for graph classification"
    - "graph u nets"
    - "edge contraction pooling for graph neural networks"
    - "a non negative factorization approach to node pooling in graph convolutional neural networks"
    - "hierarchical graph representation learning with differentiable pooling"

Connected through hyperedge HE-5 (induced by source paper "kernel graph attention network for fact verification"):
  with members:
    - "several experiments on investigating pretraining and knowledge enhanced models for natural language inference"
    - "bert for evidence retrieval and claim verification"
    - "reasoning over semantic level graph for fact checking"
    - "gear graph based evidence aggregating and reasoning for fact verification"
    - "deeper text understanding for ir with contextual neural language modeling"
    - "cedr contextualized embeddings for document ranking"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "what does bert look at an analysis of bert s attention"

Connected through hyperedge HE-6 (induced by source paper "improving attention mechanism in graph neural networks via cardinality preservation"):
  with members:
    - "self attention graph pooling"
    - "provably powerful graph networks"
    - "a comprehensive survey on graph neural networks"
    - "deepgcns can gcns go as deep as cnns"
    - "graph neural networks a review of methods and applications"
    - "hierarchical graph representation learning with differentiable pooling"
    - "weisfeiler and leman go neural higher order graph neural networks"
    - "attention models in graphs a survey"

Connected through hyperedge HE-7 (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"
    - "towards interpretable sparse graph representation learning with laplacian pooling"
    - "graph convolutional networks with eigenpooling"
    - "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)