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: "residual gated graph convnets"
  Abstract: Graph-structured data such as social networks, functional brain networks, gene regulatory networks, communications networks have brought the interest in generalizing deep learning techniques to graph domains. In this paper, we are interested to design neural networks for graphs with variable length in order to solve learning problems such as vertex classification, graph classification, graph regression, and graph generative tasks. Most existing works have focused on recurrent neural networks (RNNs) to learn meaningful representations of graphs, and more recently new convolutional neural networks (ConvNets) have been introduced. In this work, we want to compare rigorously these two fundamental families of architectures to solve graph learning tasks. We review existing graph RNN and ConvNet architectures, and propose natural extension of LSTM and ConvNet to graphs with arbitrary size. Then, we design a set of analytically controlled experiments on two basic graph problems, i.e. subgraph matching and graph clustering, to test the different architectures. Numerical results show that the proposed graph ConvNets are 3-17% more accurate and 1.5-4x faster than graph RNNs. Graph ConvNets are also 36% more accurate than variational (non-learning) techniques. Finally, the most effective graph ConvNet architecture uses gated edges and residuality. Residuality plays an essential role to learn multi-layer architectures as they provide a 10% gain of performance.

Connected through hyperedge HE-1 (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"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "cpr gcn conditional partial residual graph convolutional network in automated anatomical labeling of coronary arteries"):
  with members:
    - "graph neural networks a review of methods and applications"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "a statistical recurrent model on the manifold of symmetric positive definite matrices"
    - "neural ordinary differential equations"
    - "gated graph sequence neural networks"
    - "semi supervised classification with graph convolutional networks"
    - "deep convolutional networks on graph structured data"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "an efficient graph convolutional network technique for the travelling salesman problem"):
  with members:
    - "learning heuristics over large graphs via deep reinforcement learning"
    - "graph2seq scalable learning dynamics for graphs"
    - "machine learning for combinatorial optimization a methodological tour d horizon"
    - "learning the multiple traveling salesmen problem with permutation invariant pooling networks"
    - "combinatorial optimization with graph convolutional networks and guided tree search"
    - "geometric deep learning going beyond euclidean data"
    - "learning combinatorial optimization algorithms over graphs"
    - "encoding sentences with graph convolutional networks for semantic role labeling"

Connected through hyperedge HE-4 (induced by source paper "bayesian graph convolutional neural networks using node copying"):
  with members:
    - "fisher bures adversary graph convolutional networks"
    - "dual primal graph convolutional networks"
    - "fastgcn fast learning with graph convolutional networks via importance sampling"
    - "dynamic edge conditioned filters in convolutional neural networks on graphs"
    - "random graph models for dynamic networks"
    - "robust spatial filtering with graph convolutional neural networks"
    - "cayleynets graph convolutional neural networks with complex rational spectral filters"
    - "gated graph sequence neural networks"

Connected through hyperedge HE-5 (induced by source paper "bayesian graph convolutional neural networks using non parametric graph learning"):
  with members:
    - "fisher bures adversary graph convolutional networks"
    - "dual primal graph convolutional networks"
    - "fastgcn fast learning with graph convolutional networks via importance sampling"
    - "dynamic edge conditioned filters in convolutional neural networks on graphs"
    - "robust spatial filtering with graph convolutional neural networks"
    - "cayleynets graph convolutional neural networks with complex rational spectral filters"
    - "gated graph sequence neural networks"
    - "geometric deep learning on graphs and manifolds using mixture model cnns"

Connected through hyperedge HE-6 (induced by source paper "a two step graph convolutional decoder for molecule generation"):
  with members:
    - "graph convolutional policy network for goal directed molecular graph generation"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "automatic chemical design using a data driven continuous representation of molecules"
    - "learning deep generative models of graphs"
    - "junction tree variational autoencoder for molecular graph generation"
    - "syntax directed variational autoencoder for structured data"
    - "neural message passing for quantum chemistry"
    - "gated graph sequence neural networks"

Connected through hyperedge HE-7 (induced by source paper "neural large neighborhood search for the capacitated vehicle routing problem"):
  with members:
    - "an efficient graph convolutional network technique for the travelling salesman problem"
    - "machine learning for combinatorial optimization a methodological tour d horizon"
    - "learning the multiple traveling salesmen problem with permutation invariant pooling networks"
    - "deep learning assisted heuristic tree search for the container pre marshalling problem"
    - "learning combinatorial optimization algorithms over graphs"
    - "neural combinatorial optimization with reinforcement learning"
    - "adam a method for stochastic optimization"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-8 (induced by source paper "how to evaluate machine learning approaches for combinatorial optimization application to the travelling salesman problem"):
  with members:
    - "exact combinatorial optimization with graph convolutional neural networks"
    - "an efficient graph convolutional network technique for the travelling salesman problem"
    - "improving optimization bounds using machine learning decision diagrams meet deep reinforcement learning"
    - "attention is all you need"
    - "learning combinatorial optimization algorithms over graphs"
    - "neural combinatorial optimization with reinforcement learning"
    - "asynchronous methods for deep reinforcement learning"
    - "discriminative embeddings of latent variable models for structured data"

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)