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: "n gcn multi scale graph convolution for semi supervised node classification"
  Abstract: Graph Convolutional Networks (GCNs) have shown significant improvements in semi-supervised learning on graph-structured data. Concurrently, unsupervised learning of graph embeddings has benefited from the information contained in random walks. In this paper, we propose a model: Network of GCNs (N-GCN), which marries these two lines of work. At its core, N-GCN trains multiple instances of GCNs over node pairs discovered at different distances in random walks, and learns a combination of the instance outputs which optimizes the classification objective. Our experiments show that our proposed N-GCN model improves state-of-the-art baselines on all of the challenging node classification tasks we consider: Cora, Citeseer, Pubmed, and PPI. In addition, our proposed method has other desirable properties, including generalization to recently proposed semi-supervised learning methods such as GraphSAGE, allowing us to propose N-SAGE, and resilience to adversarial input perturbations.

Connected through hyperedge HE-1 (induced by source paper "graph inference learning for semi supervised classification"):
  with members:
    - "gaussian induced convolution for graphs"
    - "spatio temporal graph convolutional networks a deep learning framework for traffic forecasting"
    - "adversarially regularized graph autoencoder for graph embedding"
    - "topology adaptive graph convolutional networks"
    - "learning to learn meta critic networks for sample efficient learning"
    - "shift aggregate extract networks"
    - "scene graph generation by iterative message passing"
    - "model agnostic meta learning for fast adaptation of deep networks"

Connected through hyperedge HE-2 (induced by source paper "fast haar transforms for graph neural networks"):
  with members:
    - "pan path integral based convolution for deep graph neural networks"
    - "lanczosnet multi scale deep graph convolutional 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"

Connected through hyperedge HE-3 (induced by source paper "constant curvature graph convolutional networks"):
  with members:
    - "deepsphere towards an equivariant graph based spherical cnn"
    - "poincare wasserstein autoencoder"
    - "hyperbolic graph neural networks"
    - "hyperbolic graph convolutional neural networks"
    - "a comprehensive survey on graph neural networks"
    - "hyperbolic attention networks"
    - "large margin classification in hyperbolic space"
    - "link prediction based on graph neural networks"

Connected through hyperedge HE-4 (induced by source paper "simplifying graph convolutional networks"):
  with members:
    - "lanczosnet multi scale deep graph convolutional networks"
    - "label efficient semi supervised learning via graph filtering"
    - "a comprehensive survey on graph neural networks"
    - "classifier and exemplar synthesis for zero shot learning"
    - "graph neural networks a review of methods and applications"
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "predict then propagate graph neural networks meet personalized pagerank"

Connected through hyperedge HE-5 (induced by source paper "power up robust graph convolutional network against evasion attacks based on graph powering"):
  with members:
    - "mixhop higher order graph convolutional architectures via sparsified neighborhood mixing"
    - "adversarial attacks on graph neural networks via meta learning"
    - "a comprehensive survey on graph neural networks"
    - "can adversarial network attack be defended"
    - "graph powering and spectral robustness"
    - "graph neural networks a review of methods and applications"
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "adversarial attack on graph structured data"

Connected through hyperedge HE-6 (induced by source paper "rethinking kernel methods for node representation learning on graphs"):
  with members:
    - "construct dynamic graphs for hand gesture recognition via spatial temporal attention"
    - "cluster gcn an efficient algorithm for training deep and large graph convolutional networks"
    - "semantic graph convolutional networks for 3d human pose regression"
    - "graph matching networks for learning the similarity of graph structured objects"
    - "quantized densely connected u nets for efficient landmark localization"
    - "graph convolutional policy network for goal directed molecular graph generation"
    - "learning to forecast and refine residual motion for image to video generation"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"

Connected through hyperedge HE-7 (induced by source paper "pan path integral based convolution for deep graph neural networks"):
  with members:
    - "lanczosnet multi scale deep graph convolutional networks"
    - "simplifying graph convolutional networks"
    - "graph wavelet neural network"
    - "rethinking knowledge graph propagation for zero shot learning"
    - "fastgcn fast learning with graph convolutional networks via importance sampling"
    - "bootstrapped graph diffusions exposing the power of nonlinearity"
    - "neural message passing for quantum chemistry"
    - "robust spatial filtering with graph convolutional neural networks"

Connected through hyperedge HE-8 (induced by source paper "a hierarchy of graph neural networks based on learnable local features"):
  with members:
    - "on the limitations of representing functions on sets"
    - "simplifying graph convolutional networks"
    - "a comprehensive survey on graph neural networks"
    - "graph neural tangent kernel fusing graph neural networks with graph kernels"
    - "graph neural networks a review of methods and applications"
    - "weisfeiler and leman go neural higher order graph neural networks"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "how powerful are graph neural networks"

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)