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: "lanczosnet multi scale deep graph convolutional networks"
  Abstract: We propose the Lanczos network (LanczosNet), which uses the Lanczos algorithm to construct low rank approximations of the graph Laplacian for graph convolution. Relying on the tridiagonal decomposition of the Lanczos algorithm, we not only efficiently exploit multi-scale information via fast approximated computation of matrix power but also design learnable spectral filters. Being fully differentiable, LanczosNet facilitates both graph kernel learning as well as learning node embeddings. We show the connection between our LanczosNet and graph based manifold learning methods, especially the diffusion maps. We benchmark our model against several recent deep graph networks on citation networks and QM8 quantum chemistry dataset. Experimental results show that our model achieves the state-of-the-art performance in most tasks. Code is released at: \url{this https URL}.

Connected through hyperedge HE-1 (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"
    - "measuring and relieving the over smoothing problem for graph neural networks from the topological view"
    - "graph neural networks with convolutional arma filters"
    - "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-2 (induced by source paper "pushnet efficient and adaptive neural message passing"):
  with members:
    - "simplifying graph convolutional networks"
    - "fast graph representation learning with pytorch geometric"
    - "link prediction based on graph neural networks"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "attention models in graphs a survey"
    - "semi supervised learning on graphs based on local label distributions"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "relational inductive biases deep learning and graph networks"

Connected through hyperedge HE-3 (induced by source paper "ripple walk training a subgraph based training framework for large and deep graph neural network"):
  with members:
    - "the truly deep graph convolutional networks for node classification"
    - "pairnorm tackling oversmoothing in gnns"
    - "graphsaint graph sampling based inductive learning method"
    - "mixhop higher order graph convolutional architectures via sparsified neighborhood mixing"
    - "cluster gcn an efficient algorithm for training deep and large graph convolutional networks"
    - "gresnet graph residual network for reviving deep gnns from suspended animation"
    - "graph star net for generalized multi task learning"
    - "graph convolutional neural networks for web scale recommender systems"

Connected through hyperedge HE-4 (induced by source paper "bilinear graph neural network with node interactions"):
  with members:
    - "simplifying graph convolutional networks"
    - "graph wavelet neural network"
    - "hyperbolic graph convolutional neural networks"
    - "neural graph collaborative filtering"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "graph convolutional networks for text classification"
    - "deep learning on graphs a survey"
    - "rethinking knowledge graph propagation for zero shot learning"

Connected through hyperedge HE-5 (induced by source paper "permutation invariant graph generation via score based generative modeling"):
  with members:
    - "universal invariant and equivariant graph neural networks"
    - "generative modeling by estimating gradients of the data distribution"
    - "graph normalizing flows"
    - "exploring randomly wired neural networks for image recognition"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "representation learning on graphs with jumping knowledge networks"
    - "learning deep generative models of graphs"
    - "junction tree variational autoencoder for molecular graph generation"

Connected through hyperedge HE-6 (induced by source paper "scattering gcn overcoming oversmoothness in graph convolutional networks"):
  with members:
    - "mixhop higher order graph convolutional architectures via sparsified neighborhood mixing"
    - "spectral multigraph networks for discovering and fusing relationships in molecules"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "diffusion scattering transforms on graphs"
    - "geometric scattering for graph data analysis"
    - "neural message passing for quantum chemistry"
    - "geometric deep learning going beyond euclidean data"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-7 (induced by source paper "graph prolongation convolutional networks explicitly multiscale machine learning on graphs with applications to modeling of biological systems"):
  with members:
    - "novel diffusion derived distance measures for graphs"
    - "a gentle introduction to deep learning for graphs"
    - "hierarchical graph representation learning with differentiable pooling"
    - "multilevel artificial neural network training for spatially correlated learning"
    - "tensor graph convolutional neural network"
    - "pymanopt a python toolbox for optimization on manifolds using automatic differentiation"
    - "multigrid neural architectures"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-8 (induced by source paper "fast haar transforms for graph neural networks"):
  with members:
    - "pan path integral based convolution for deep graph neural networks"
    - "simplifying graph convolutional networks"
    - "graph wavelet neural network"
    - "haarpooling graph pooling with compressive haar basis"
    - "a comprehensive survey on graph neural networks"
    - "graph neural networks a review of methods and applications"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "n gcn multi scale graph convolution for semi supervised node classification"

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)