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: "graph convolutional networks for classification with a structured label space"
  Abstract: It is a usual practice to ignore any structural information underlying classes in multi-class classification. In this paper, we propose a graph convolutional network (GCN) augmented neural network classifier to exploit a known, underlying graph structure of labels. The proposed approach resembles an (approximate) inference procedure in, for instance, a conditional random field (CRF). We evaluate the proposed approach on document classification and object recognition and report both accuracies and graph-theoretic metrics that correspond to the consistency of the model's prediction. The experiment results reveal that the proposed model outperforms a baseline method which ignores the graph structures of a label space in terms of graph-theoretic metrics.

Connected through hyperedge HE-1 (induced by source paper "long tail relation extraction via knowledge graph embeddings and graph convolution networks"):
  with members:
    - "dsgan generative adversarial training for distant supervision relation extraction"
    - "reinforcement learning for relation classification from noisy data"
    - "deep residual learning for weakly supervised relation extraction"
    - "graph convolutional encoders for syntax aware neural machine translation"
    - "encoding sentences with graph convolutional networks for semantic role labeling"
    - "dependency parsing with dilated iterated graph cnns"
    - "incorporating relation paths in neural relation extraction"
    - "jointly extracting relations with class ties via effective deep ranking"

Connected through hyperedge HE-2 (induced by source paper "attentional multilabel learning over graphs a message passing approach"):
  with members:
    - "graph memory networks for molecular activity prediction"
    - "junction tree variational autoencoder for molecular graph generation"
    - "network representation learning a survey"
    - "order free rnn with visual attention for multi label classification"
    - "representation learning on graphs methods and applications"
    - "neural message passing for quantum chemistry"
    - "hcp a flexible cnn framework for multi label image classification"
    - "column networks for collective classification"

Connected through hyperedge HE-3 (induced by source paper "semi supervised learning on graphs based on local label distributions"):
  with members:
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "struc2vec learning node representations from structural identity"
    - "lasagne locality and structure aware graph node embedding"
    - "representation learning on graphs methods and applications"
    - "neural message passing for quantum chemistry"
    - "gated graph sequence neural networks"
    - "node2vec scalable feature learning for networks"
    - "geometric deep learning on graphs and manifolds using mixture model cnns"

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)