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: "factorising amr generation through syntax"
  Abstract: Generating from Abstract Meaning Representation (AMR) is an underspecified problem, as many syntactic decisions are not constrained by the semantic graph. To explicitly account for this underspecification, we break down generating from AMR into two steps: first generate a syntactic structure, and then generate the surface form. We show that decomposing the generation process this way leads to state-of-the-art single model performance generating from AMR without additional unlabelled data. We also demonstrate that we can generate meaning-preserving syntactic paraphrases of the same AMR graph, as judged by humans.

Connected through hyperedge HE-1 (induced by source paper "graph transformer for graph to sequence learning"):
  with members:
    - "text generation from knowledge graphs with graph transformers"
    - "analyzing multi head self attention specialized heads do the heavy lifting the rest can be pruned"
    - "transformer xl attentive language models beyond a fixed length context"
    - "structural neural encoders for amr to text generation"
    - "densely connected graph convolutional networks for graph to sequence learning"
    - "toward abstractive summarization using semantic representations"
    - "graph2seq graph to sequence learning with attention based neural networks"
    - "a graph to sequence model for amr to text generation"

Connected through hyperedge HE-2 (induced by source paper "modeling graph structure in transformer for better amr to text generation"):
  with members:
    - "text generation from knowledge graphs with graph transformers"
    - "structural neural encoders for amr to text generation"
    - "densely connected graph convolutional networks for graph to sequence learning"
    - "self attention with relative position representations"
    - "graph to sequence learning using gated graph neural networks"
    - "a graph to sequence model for amr to text generation"
    - "attention is all you need"
    - "opennmt open source toolkit for neural machine translation"

Connected through hyperedge HE-3 (induced by source paper "enhancing amr to text generation with dual graph representations"):
  with members:
    - "text generation from knowledge graphs with graph transformers"
    - "densely connected graph convolutional networks for graph to sequence learning"
    - "semantic neural machine translation using amr"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a graph to sequence model for amr to text generation"
    - "how powerful are graph neural networks"
    - "abstract meaning representation for multi document summarization"
    - "deep graph convolutional encoders for structured data to text generation"

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)