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: "dual primal graph convolutional networks"
  Abstract: In recent years, there has been a surge of interest in developing deep learning methods for non-Euclidean structured data such as graphs. In this paper, we propose Dual-Primal Graph CNN, a graph convolutional architecture that alternates convolution-like operations on the graph and its dual. Our approach allows to learn both vertex- and edge features and generalizes the previous graph attention (GAT) model. We provide extensive experimental validation showing state-of-the-art results on a variety of tasks tested on established graph benchmarks, including CORA and Citeseer citation networks as well as MovieLens, Flixter, Douban and Yahoo Music graph-guided recommender systems.

Connected through hyperedge HE-1 (induced by source paper "differentiable graph module dgm graph convolutional networks"):
  with members:
    - "brain signal classification via learning connectivity structure"
    - "graph neural networks with convolutional arma filters"
    - "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"
    - "neural nearest neighbors networks"

Connected through hyperedge HE-2 (induced by source paper "line hypergraph convolution network applying graph convolution for hypergraphs"):
  with members:
    - "a comprehensive survey on graph neural networks"
    - "hypergraph neural networks"
    - "hypergcn a new method of training graph convolutional networks on hypergraphs"
    - "submodular hypergraphs p laplacians cheeger inequalities and spectral clustering"
    - "fastgcn fast learning with graph convolutional networks via importance sampling"
    - "representation learning on graphs methods and applications"
    - "semi supervised classification with graph convolutional networks"
    - "learning convolutional neural networks for graphs"

Connected through hyperedge HE-3 (induced by source paper "meshcnn a network with an edge"):
  with members:
    - "spherical cnns on unstructured grids"
    - "generating 3d faces using convolutional mesh autoencoders"
    - "alignet partial shape agnostic alignment via unsupervised learning"
    - "hierarchical graph representation learning with differentiable pooling"
    - "point convolutional neural networks by extension operators"
    - "multi directional geodesic neural networks via equivariant convolution"
    - "learning 3d shapes as multi layered height maps using 2d convolutional networks"
    - "dynamic graph cnn for learning on point clouds"

Connected through hyperedge HE-4 (induced by source paper "provably powerful graph networks"):
  with members:
    - "universal invariant and equivariant graph neural networks"
    - "towards a practical k dimensional weisfeiler leman algorithm"
    - "on the universality of invariant networks"
    - "a survey on graph kernels"
    - "fast graph representation learning with pytorch geometric"
    - "relational pooling for graph representations"
    - "hierarchical graph representation learning with differentiable pooling"
    - "weisfeiler and leman go neural higher order graph neural networks"

Connected through hyperedge HE-5 (induced by source paper "fake news detection on social media using geometric deep learning"):
  with members:
    - "influence of fake news in twitter during the 2016 us presidential election"
    - "studying fake news via network analysis detection and mitigation"
    - "on the convergence of adam and beyond"
    - "fake news a survey of research detection methods and opportunities"
    - "dynamic graph cnn for learning on point clouds"
    - "motifnet a motif based graph convolutional network for directed graphs"
    - "peernets exploiting peer wisdom against adversarial attacks"
    - "self normalizing neural networks"

Connected through hyperedge HE-6 (induced by source paper "bayesian graph convolutional neural networks using node copying"):
  with members:
    - "fisher bures adversary graph convolutional networks"
    - "residual gated graph convnets"
    - "fastgcn fast learning with graph convolutional networks via importance sampling"
    - "dynamic edge conditioned filters in convolutional neural networks on graphs"
    - "random graph models for dynamic networks"
    - "robust spatial filtering with graph convolutional neural networks"
    - "cayleynets graph convolutional neural networks with complex rational spectral filters"
    - "gated graph sequence neural networks"

Connected through hyperedge HE-7 (induced by source paper "composition based multi relational graph convolutional networks"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "hypernetwork knowledge graph embeddings"
    - "fast graph representation learning with pytorch geometric"
    - "graph to sequence learning using gated graph neural networks"
    - "how powerful are graph neural networks"
    - "end to end structure aware convolutional networks for knowledge base completion"
    - "a novel embedding model for knowledge base completion based on convolutional neural network"
    - "kbgan adversarial learning for knowledge graph embeddings"

Connected through hyperedge HE-8 (induced by source paper "dimensional reweighting graph convolutional networks"):
  with members:
    - "mixhop higher order graph convolutional architectures via sparsified neighborhood mixing"
    - "graphnas graph neural architecture search with reinforcement learning"
    - "geniepath graph neural networks with adaptive receptive paths"
    - "gaan gated attention networks for learning on large and spatiotemporal graphs"
    - "large scale learnable graph convolutional networks"
    - "adaptive sampling towards fast graph representation learning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "fastgcn fast learning with graph convolutional networks via importance sampling"

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)