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: "bootstrapped graph diffusions exposing the power of nonlinearity"
  Abstract: Graph-based semi-supervised learning (SSL) algorithms predict labels for all nodes based on provided labels of a small set of seed nodes. Classic methods capture the graph structure through some underlying diffusion process that propagates through the graph edges. Spectral diffusion, which includes personalized page rank and label propagation, propagates through random walks. Social diffusion propagates through shortest paths. A common ground to these diffusions is their {\em linearity}, which does not distinguish between contributions of few "strong" relations and many "weak" relations. #R##N#Recently, non-linear methods such as node embeddings and graph convolutional networks (GCN) demonstrated a large gain in quality for SSL tasks. These methods introduce multiple components and greatly vary on how the graph structure, seed label information, and other features are used. #R##N#We aim here to study the contribution of non-linearity, as an isolated ingredient, to the performance gain. To do so, we place classic linear graph diffusions in a self-training framework. Surprisingly, we observe that SSL using the resulting {\em bootstrapped diffusions} not only significantly improves over the respective non-bootstrapped baselines but also outperform state-of-the-art non-linear SSL methods. Moreover, since the self-training wrapper retains the scalability of the base method, we obtain both higher quality and better scalability.

Connected through hyperedge HE-1 (induced by source paper "diffusion improves graph learning"):
  with members:
    - "pan path integral based convolution for deep graph neural networks"
    - "graph neural networks with convolutional arma filters"
    - "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"

Connected through hyperedge HE-2 (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-3 (induced by source paper "certifiable robustness to graph perturbations"):
  with members:
    - "transferring robustness for graph neural network against poisoning attacks"
    - "adversarial examples on graph data deep insights into attack and defense"
    - "topology attack and defense for graph neural networks an optimization perspective"
    - "adversarial robustness of similarity based link prediction"
    - "adversarial defense framework for graph neural network"
    - "simplifying graph convolutional networks"
    - "learning graph neural networks with noisy labels"
    - "fast graph representation learning with pytorch geometric"

Connected through hyperedge HE-4 (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"
    - "n gcn multi scale graph convolution for semi supervised node classification"
    - "rethinking knowledge graph propagation for zero shot learning"
    - "fastgcn fast learning with graph convolutional networks via importance sampling"
    - "neural message passing for quantum chemistry"
    - "robust spatial filtering with graph convolutional neural networks"

Connected through hyperedge HE-5 (induced by source paper "dynamic self training framework for graph convolutional networks"):
  with members:
    - "multi stage self supervised learning for graph convolutional networks"
    - "pitfalls of graph neural network evaluation"
    - "representation learning on graphs with jumping knowledge networks"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "fastgcn fast learning with graph convolutional networks via importance sampling"
    - "predicting the effectiveness of self training application to sentiment classification"
    - "node2vec scalable feature learning for networks"
    - "geometric deep learning on graphs and manifolds using mixture model cnns"

Connected through hyperedge HE-6 (induced by source paper "adagcn adaboosting graph convolutional networks into deep models"):
  with members:
    - "lanczosnet multi scale deep graph convolutional networks"
    - "simplifying graph convolutional networks"
    - "predict then propagate graph neural networks meet personalized pagerank"
    - "pitfalls of graph neural network evaluation"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "n gcn multi scale graph convolution for semi supervised node classification"
    - "fastgcn fast learning with graph convolutional networks via importance sampling"
    - "learning deep resnet blocks sequentially using boosting theory"

Connected through hyperedge HE-7 (induced by source paper "predict then propagate graph neural networks meet personalized pagerank"):
  with members:
    - "mean field theory of graph neural networks in graph partitioning"
    - "graph convolutional neural networks for web scale recommender systems"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "pitfalls of graph neural network evaluation"
    - "representation learning on graphs with jumping knowledge networks"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "n gcn multi scale graph convolution for semi supervised node classification"
    - "fastgcn fast learning with graph convolutional networks via importance sampling"

Connected through hyperedge HE-8 (induced by source paper "every node counts self ensembling graph convolutional networks for semi supervised learning"):
  with members:
    - "adaptive graph convolutional neural networks"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "semi supervised user geolocation via graph convolutional networks"
    - "n gcn multi scale graph convolution for semi supervised node classification"
    - "fusion graph convolutional networks"
    - "dynamic graph cnn for learning on point clouds"
    - "random erasing data augmentation"
    - "feastnet feature steered graph convolutions for 3d shape analysis"

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)