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: "large scale learnable graph convolutional networks"
  Abstract: Convolutional neural networks (CNNs) have achieved great success on grid-like data such as images, but face tremendous challenges in learning from more generic data such as graphs. In CNNs, the trainable local filters enable the automatic extraction of high-level features. The computation with filters requires a fixed number of ordered units in the receptive fields. However, the number of neighboring units is neither fixed nor are they ordered in generic graphs, thereby hindering the applications of convolutional operations. Here, we address these challenges by proposing the learnable graph convolutional layer (LGCL). LGCL automatically selects a fixed number of neighboring nodes for each feature based on value ranking in order to transform graph data into grid-like structures in 1-D format, thereby enabling the use of regular convolutional operations on generic graphs. To enable model training on large-scale graphs, we propose a sub-graph training method to reduce the excessive memory and computational resource requirements suffered by prior methods on graph convolutions. Our experimental results on node classification tasks in both transductive and inductive learning settings demonstrate that our methods can achieve consistently better performance on the Cora, Citeseer, Pubmed citation network, and protein-protein interaction network datasets. Our results also indicate that the proposed methods using sub-graph training strategy are more efficient as compared to prior approaches.

Connected through hyperedge HE-1 (induced by source paper "transferring robustness for graph neural network against poisoning attacks"):
  with members:
    - "adversarial examples on graph data deep insights into attack and defense"
    - "power up robust graph convolutional network against evasion attacks based on graph powering"
    - "topology attack and defense for graph neural networks an optimization perspective"
    - "graph neural networks with generated parameters for relation extraction"
    - "graph few shot learning via knowledge transfer"
    - "graph convolutional networks with eigenpooling"
    - "graph neural networks with high order feature interactions"
    - "regional homogeneity towards learning transferable universal adversarial perturbations against defenses"

Connected through hyperedge HE-2 (induced by source paper "adargcn adaptive aggregation gcn for few shot learning"):
  with members:
    - "dual graph convolutional network for semantic segmentation"
    - "meta learning with differentiable convex optimization"
    - "infinite mixture prototypes for few shot learning"
    - "t gcn a temporal graph convolutional network for traffic prediction"
    - "edge labeling graph neural network for few shot learning"
    - "generating classification weights with gnn denoising autoencoders for few shot learning"
    - "semantic graph convolutional networks for 3d human pose regression"
    - "a closer look at few shot classification"

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"
    - "lanczosnet multi scale deep graph convolutional networks"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "attributed multi relational attention network for fact checking url recommendation"):
  with members:
    - "learning from fact checkers analysis and generation of fact checking language"
    - "neural graph collaborative filtering"
    - "the rise of guardians fact checking url recommendation to combat fake news"
    - "harmonious attention network for person re identification"
    - "collaborative memory network for recommendation systems"
    - "semi supervised content based detection of misinformation via tensor embeddings"
    - "nais neural attentive item similarity model for recommendation"
    - "fake news detection in social networks via crowd signals"

Connected through hyperedge HE-5 (induced by source paper "uwb gcn accelerating graph convolutional networks through runtime workload rebalancing"):
  with members:
    - "graphblast a high performance linear algebra based graph framework on the gpu"
    - "a comprehensive survey on graph neural networks"
    - "packing sparse convolutional neural networks for efficient systolic array implementations column combining under joint optimization"
    - "graph neural networks a review of methods and applications"
    - "graph convolutional neural networks for web scale recommender systems"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "modeling polypharmacy side effects with graph convolutional networks"
    - "how powerful are graph neural networks"

Connected through hyperedge HE-6 (induced by source paper "geom gcn geometric graph convolutional networks"):
  with members:
    - "multi scale attributed node embedding"
    - "simplifying graph convolutional networks"
    - "mcne an end to end framework for learning multiple conditional network representations of social network"
    - "community detection on networks with ricci flow"
    - "learning continuous hierarchies in the lorentz model of hyperbolic geometry"
    - "representation learning on graphs with jumping knowledge networks"
    - "relational inductive biases deep learning and graph networks"
    - "fastgcn fast learning with graph convolutional networks via importance sampling"

Connected through hyperedge HE-7 (induced by source paper "unsupervised hierarchical graph representation learning by mutual information maximization"):
  with members:
    - "fobe and hobe first and high order bipartite embeddings"
    - "predict then propagate graph neural networks meet personalized pagerank"
    - "janossy pooling learning deep permutation invariant functions for variable size inputs"
    - "harp hierarchical representation learning for networks"
    - "neural message passing for quantum chemistry"
    - "graph2vec learning distributed representations of graphs"
    - "predicting organic reaction outcomes with weisfeiler lehman network"
    - "node2vec scalable feature learning for networks"

Connected through hyperedge HE-8 (induced by source paper "monstor an inductive approach for estimating and maximizing influence over unseen social networks"):
  with members:
    - "disco influence maximization meets network embedding and deep learning"
    - "irie scalable and robust influence maximization in social 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)