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: "lovasz convolutional networks"
  Abstract: Semi-supervised learning on graph structured data has received significant attention with the recent introduction of Graph Convolution Networks (GCN). While traditional methods have focused on optimizing a loss augmented with Laplacian regularization framework, GCNs perform an implicit Laplacian type regularization to capture local graph structure. In this work, we propose Lovasz Convolutional Network (LCNs) which are capable of incorporating global graph properties. LCNs achieve this by utilizing Lovasz's orthonormal embeddings of the nodes. We analyse local and global properties of graphs and demonstrate settings where LCNs tend to work better than GCNs. We validate the proposed method on standard random graph models such as stochastic block models (SBM) and certain community structure based graphs where LCNs outperform GCNs and learn more intuitive embeddings. We also perform extensive binary and multi-class classification experiments on real world datasets to demonstrate LCN's effectiveness. In addition to simple graphs, we also demonstrate the use of LCNs on hyper-graphs by identifying settings where they are expected to work better than GCNs.

Connected through hyperedge HE-1 (induced by source paper "neural graph embedding methods for natural language processing"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "confidence based graph convolutional networks for semi supervised learning"
    - "hypernetwork knowledge graph embeddings"
    - "cesi canonicalizing open knowledge bases using embeddings and side information"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "representation learning on graphs with jumping knowledge networks"
    - "graph to sequence learning using gated graph neural networks"
    - "improving distantly supervised relation extraction using word and entity based attention"

Connected through hyperedge HE-2 (induced by source paper "confidence based graph convolutional networks for semi supervised learning"):
  with members:
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "representation learning on graphs with jumping knowledge networks"
    - "graph partition neural networks for semi supervised classification"
    - "reside improving distantly supervised neural relation extraction using side information"
    - "graph convolutional encoders for syntax aware neural machine translation"
    - "geometric deep learning going beyond euclidean data"
    - "encoding sentences with graph convolutional networks for semantic role labeling"
    - "gated graph sequence neural networks"

Connected through hyperedge HE-3 (induced by source paper "graph convolutional networks analysis improvements and results"):
  with members:
    - "confidence based graph convolutional networks for semi supervised learning"
    - "learning combinations of activation functions"
    - "dual primal graph convolutional networks"
    - "large scale learnable graph convolutional networks"
    - "graph partition neural networks for semi supervised classification"
    - "encoding sentences with graph convolutional networks for semantic role labeling"
    - "gated graph sequence neural networks"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "unsupervised attributed multiplex network embedding"):
  with members:
    - "self attention graph pooling"
    - "mvn2vec preservation and collaboration in multi view network embedding"
    - "multi dimensional graph convolutional networks"
    - "mine mutual information neural estimation"
    - "struc2vec learning node representations from structural identity"
    - "an attention based collaboration framework for multi view network representation learning"
    - "principled multilayer network embedding"
    - "attributed network embedding for learning in a dynamic environment"

Connected through hyperedge HE-5 (induced by source paper "incorporating syntactic and semantic information in word embeddings using graph convolutional networks"):
  with members:
    - "confidence based graph convolutional networks for semi supervised learning"
    - "dating documents using graph convolution networks"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "graph convolutional networks for text classification"
    - "deep contextualized word representations"
    - "higher order coreference resolution with coarse to fine inference"
    - "reside improving distantly supervised neural relation extraction using side information"
    - "linguistically informed self attention for semantic role labeling"

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)