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: "learning discrete structures for graph neural networks"
  Abstract: Graph neural networks (GNNs) are a popular class of machine learning models whose major advantage is their ability to incorporate a sparse and discrete dependency structure between data points. Unfortunately, GNNs can only be used when such a graph-structure is available. In practice, however, real-world graphs are often noisy and incomplete or might not be available at all. With this work, we propose to jointly learn the graph structure and the parameters of graph convolutional networks (GCNs) by approximately solving a bilevel program that learns a discrete probability distribution on the edges of the graph. This allows one to apply GCNs not only in scenarios where the given graph is incomplete or corrupted but also in those where a graph is not available. We conduct a series of experiments that analyze the behavior of the proposed method and demonstrate that it outperforms related methods by a significant margin.

Connected through hyperedge HE-1 (induced by source paper "an end to end graph convolutional kernel support vector machine"):
  with members:
    - "scalable global alignment graph kernel using random features from node embedding to graph embedding"
    - "a fair comparison of graph neural networks for graph classification"
    - "on graph classification networks datasets and baselines"
    - "fast graph representation learning with pytorch geometric"
    - "a comprehensive survey on graph neural networks"
    - "representation learning on graphs with jumping knowledge networks"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "deep learning on graphs a survey"

Connected through hyperedge HE-2 (induced by source paper "graph preserving grid layout a simple graph drawing method for graph classification using cnns"):
  with members:
    - "a deep generative model for graph layout"
    - "semi supervised graph classification a hierarchical graph perspective"
    - "graph u nets"
    - "simplifying graph convolutional networks"
    - "graph star net for generalized multi task learning"
    - "a comprehensive survey on graph neural networks"
    - "weisfeiler and leman go neural higher order graph neural networks"
    - "graphrnn generating realistic graphs with deep auto regressive models"

Connected through hyperedge HE-3 (induced by source paper "attributed graph learning with 2 d graph convolution"):
  with members:
    - "lanczosnet multi scale deep graph convolutional networks"
    - "gmnn graph markov neural networks"
    - "mixhop higher order graph convolutional architectures via sparsified neighborhood mixing"
    - "attributed graph clustering via adaptive graph convolution"
    - "label efficient semi supervised learning via graph filtering"
    - "a flexible generative framework for graph based semi supervised learning"
    - "graph neural networks a review of methods and applications"
    - "deep learning on graphs a survey"

Connected through hyperedge HE-4 (induced by source paper "deep iterative and adaptive learning for graph neural networks"):
  with members:
    - "graphflow exploiting conversation flow with graph neural networks for conversational machine comprehension"
    - "graph convolutional transformer learning the graphical structure of electronic health records"
    - "reinforcement learning based graph to sequence model for natural question generation"
    - "dyngraph2seq dynamic graph to sequence interpretable learning for health stage prediction in online health forums"
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "sql to text generation with graph to sequence model"
    - "adaptive graph convolutional neural networks"
    - "exploiting rich syntactic information for semantic parsing with graph to sequence model"

Connected through hyperedge HE-5 (induced by source paper "neighborhood enlargement in graph neural networks"):
  with members:
    - "gmnn graph markov neural networks"
    - "graph neural networks with convolutional arma filters"
    - "graph u nets"
    - "hierarchical graph representation learning with differentiable pooling"
    - "representation learning on graphs with jumping knowledge networks"
    - "how powerful are graph neural networks"
    - "anonymous walk embeddings"
    - "mine mutual information neural estimation"

Connected through hyperedge HE-6 (induced by source paper "brain signal classification via learning connectivity structure"):
  with members:
    - "classification of eeg based brain connectivity networks in schizophrenia using a multi domain connectome convolutional neural network"
    - "a dilated inception network for visual saliency prediction"
    - "a comprehensive survey on graph neural networks"
    - "convolutional neural network approach for eeg based emotion recognition using brain connectivity and its spatial information"
    - "relational inductive biases deep learning and graph networks"
    - "deep learning with convolutional neural networks for eeg decoding and visualization"
    - "neural message passing for quantum chemistry"
    - "distance metric learning using graph convolutional networks application to functional brain networks"

Connected through hyperedge HE-7 (induced by source paper "graph revised convolutional network"):
  with members:
    - "simplifying graph convolutional networks"
    - "fast graph representation learning with pytorch geometric"
    - "a comprehensive survey on graph neural networks"
    - "link prediction based on graph neural networks"
    - "graph convolutional neural networks for web scale recommender systems"
    - "pitfalls of graph neural network evaluation"
    - "dynamic graph cnn for learning on point clouds"
    - "large scale point cloud semantic segmentation with superpoint graphs"

Connected through hyperedge HE-8 (induced by source paper "first and complementary neighborhood combination of adjacency matrix for graph learning"):
  with members:
    - "gmnn graph markov neural networks"
    - "graph neural networks with convolutional arma filters"
    - "mixhop higher order graph convolutional architectures via sparsified neighborhood mixing"
    - "spectral multigraph networks for discovering and fusing relationships in molecules"
    - "representation learning on graphs with jumping knowledge networks"
    - "how powerful are graph neural networks"
    - "mine mutual information neural estimation"
    - "dynamic edge conditioned filters in convolutional neural networks on graphs"

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)