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 neural networks with convolutional arma filters"
  Abstract: Popular graph neural networks implement convolution operations on graphs based on polynomial filters defined in the spectral domain. In this paper, we propose a novel graph convolutional layer inspired by the auto-regressive moving average (ARMA) filters that, compared to the polynomial ones, are more robust and provide a more flexible graph frequency response. We propose a neural network implementation of the ARMA filter with a recursive and distributed formulation, obtaining a convolutional layer that is efficient to train, localized in the node space, and can be transferred to new graphs unseen during training. We report a spectral analysis of the proposed trainable filter, as well as experiments on four major downstream tasks: semi-supervised node classification, graph signal classification, graph classification, and graph regression. The results show that ARMA filters bring significant improvements over graph neural networks based on polynomial filters.

Connected through hyperedge HE-1 (induced by source paper "bridging the gap between spatial and spectral domains a survey on graph neural networks"):
  with members:
    - "anti money laundering in bitcoin experimenting with graph convolutional networks for financial forensics"
    - "a fair comparison of graph neural networks for graph classification"
    - "mixhop higher order graph convolutional architectures via sparsified neighborhood mixing"
    - "what graph neural networks cannot learn depth vs width"
    - "gnn explainer a tool for post hoc explanation of graph neural networks"
    - "simplifying graph convolutional networks"
    - "learning individual treatment effects from networked observational data"
    - "explainability techniques for graph convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "spectral graph attention network"):
  with members:
    - "power up robust graph convolutional network against evasion attacks based on graph powering"
    - "stabilizing gans with octave convolutions"
    - "a restricted black box adversarial framework towards attacking graph embedding models"
    - "lanczosnet multi scale deep graph convolutional networks"
    - "measuring and relieving the over smoothing problem for graph neural networks from the topological view"
    - "dual graph attention networks for deep latent representation of multifaceted social effects in recommender systems"
    - "semi supervised graph classification a hierarchical graph perspective"
    - "graph wavelet neural network"

Connected through hyperedge HE-3 (induced by source paper "edgenets edge varying graph neural networks"):
  with members:
    - "dfnets spectral cnns for graphs with feedback looped filters"
    - "stability properties of graph neural networks"
    - "on the transferability of spectral graph filters"
    - "generalizing graph convolutional neural networks with edge variant recursions on graphs"
    - "a comprehensive survey on graph neural networks"
    - "graph neural networks a review of methods and applications"
    - "attention models in graphs a survey"
    - "deep learning on graphs a survey"

Connected through hyperedge HE-4 (induced by source paper "differentiable graph module dgm graph convolutional networks"):
  with members:
    - "brain signal classification via learning connectivity structure"
    - "stochastic beams and where to find them the gumbel top k trick for sampling sequences without replacement"
    - "link prediction based on graph neural networks"
    - "n body networks a covariant hierarchical neural network architecture for learning atomic potentials"
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "adaptive graph convolutional neural networks"
    - "dual primal graph convolutional networks"
    - "neural nearest neighbors networks"

Connected through hyperedge HE-5 (induced by source paper "adaptive propagation graph convolutional network"):
  with members:
    - "constant time graph neural networks"
    - "diffusion improves graph learning"
    - "fast graph representation learning with pytorch geometric"
    - "a gentle introduction to deep learning for graphs"
    - "a comprehensive survey on graph neural networks"
    - "predict then propagate graph neural networks meet personalized pagerank"
    - "weisfeiler and leman go neural higher order graph neural networks"
    - "representation learning on graphs with jumping knowledge networks"

Connected through hyperedge HE-6 (induced by source paper "human action recognition with multi laplacian graph convolutional networks"):
  with members:
    - "monte carlo convolution for learning on non uniformly sampled point clouds"
    - "simplifying graph convolutional networks"
    - "graph wavelet neural network"
    - "graph convolutional neural networks via scattering"
    - "a comprehensive survey on graph neural networks"
    - "part based graph convolutional network for action recognition"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "predict then propagate graph neural networks meet personalized pagerank"

Connected through hyperedge HE-7 (induced by source paper "diffusion improves graph learning"):
  with members:
    - "pan path integral based convolution for deep graph neural networks"
    - "mixhop higher order graph convolutional architectures via sparsified neighborhood mixing"
    - "simplifying graph convolutional networks"
    - "fast graph representation learning with pytorch geometric"
    - "optimizing generalized pagerank methods for seed expansion community detection"
    - "node embedding with adaptive similarities for scalable learning over graphs"
    - "graph convolutional neural networks for web scale recommender systems"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"

Connected through hyperedge HE-8 (induced by source paper "hierarchical representation learning in graph neural networks with node decimation pooling"):
  with members:
    - "mincut pooling in graph neural networks"
    - "understanding attention and generalization in graph neural networks"
    - "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"

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)