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: "dating documents using graph convolution networks"
  Abstract: Document date is essential for many important tasks, such as document retrieval, summarization, event detection, etc. While existing approaches for these tasks assume accurate knowledge of the document date, this is not always available, especially for arbitrary documents from the Web. Document Dating is a challenging problem which requires inference over the temporal structure of the document. Prior document dating systems have largely relied on handcrafted features while ignoring such document internal structures. In this paper, we propose NeuralDater, a Graph Convolutional Network (GCN) based document dating approach which jointly exploits syntactic and temporal graph structures of document in a principled way. To the best of our knowledge, this is the first application of deep learning for the problem of document dating. Through extensive experiments on real-world datasets, we find that NeuralDater significantly outperforms state-of-the-art baseline by 19% absolute (45% relative) accuracy points.

Connected through hyperedge HE-1 (induced by source paper "asap adaptive structure aware pooling for learning hierarchical graph representations"):
  with members:
    - "graph convolutional networks with eigenpooling"
    - "graph u nets"
    - "fast graph representation learning with pytorch geometric"
    - "composition based multi relational graph convolutional networks"
    - "hierarchical graph representation learning with differentiable pooling"
    - "weisfeiler and leman go neural higher order graph neural networks"
    - "mt cgcnn integrating crystal graph convolutional neural network with multitask learning for material property prediction"
    - "reside improving distantly supervised neural relation extraction using side information"

Connected through hyperedge HE-2 (induced by source paper "lattice cnns for matching based chinese question answering"):
  with members:
    - "chinese ner using lattice lstm"
    - "subword encoding in lattice lstm for chinese word segmentation"
    - "a graph to sequence model for amr to text generation"
    - "cross sentence n ary relation extraction with graph lstms"
    - "improved neural relation detection for knowledge base question answering"
    - "matchzoo a toolkit for deep text matching"
    - "encoding sentences with graph convolutional networks for semantic role labeling"
    - "bilateral multi perspective matching for natural language sentences"

Connected through hyperedge HE-3 (induced by source paper "aspect based sentiment classification with aspect specific graph convolutional networks"):
  with members:
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "transformation networks for target oriented sentiment classification"
    - "graph convolutional networks for text classification"
    - "aspect level sentiment classification with attention over attention neural networks"
    - "aspect based sentiment analysis with gated convolutional networks"
    - "encoding sentences with graph convolutional networks for semantic role labeling"
    - "interactive attention networks for aspect level sentiment classification"
    - "aspect level sentiment classification with deep memory network"

Connected through hyperedge HE-4 (induced by source paper "inter sentence relation extraction with document level graph convolutional neural network"):
  with members:
    - "simultaneously self attending to all mentions for full abstract biological relation extraction"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "cross sentence n ary relation extraction with graph lstms"
    - "graph based neural multi document summarization"
    - "encoding sentences with graph convolutional networks for semantic role labeling"
    - "unsupervised ranking model for entity coreference resolution"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "end to end relation extraction using lstms on sequences and tree structures"

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"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "lovasz convolutional networks"
    - "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.CL(Computation and Language)