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: "amr to text generation with synchronous node replacement grammar"
  Abstract: This paper addresses the task of AMR-to-text generation by leveraging synchronous node replacement grammar. During training, graph-to-string rules are learned using a heuristic extraction algorithm. At test time, a graph transducer is applied to collapse input AMRs and generate output sentences. Evaluated on SemEval-2016 Task 8, our method gives a BLEU score of 25.62, which is the best reported so far.

Connected through hyperedge HE-1 (induced by source paper "modeling global and local node contexts for text generation from knowledge graphs"):
  with members:
    - "text generation from knowledge graphs with graph transformers"
    - "understanding the representation power of graph neural networks in learning graph topology"
    - "graph transformer for graph to sequence learning"
    - "structural neural encoders for amr to text generation"
    - "neural data to text generation a comparison between pipeline and end to end architectures"
    - "densely connected graph convolutional networks for graph to sequence learning"
    - "enhancing amr to text generation with dual graph representations"
    - "step by step separating planning from realization in neural data to text generation"

Connected through hyperedge HE-2 (induced by source paper "amr similarity metrics from principles"):
  with members:
    - "core semantic first a top down approach for amr parsing"
    - "automatic accuracy prediction for amr parsing"
    - "structural neural encoders for amr to text generation"
    - "sembleu a robust metric for amr parsing evaluation"
    - "amr parsing as sequence to graph transduction"
    - "sema an extended semantic evaluation metric for amr"
    - "amr normalization for fairer evaluation"
    - "a graph to sequence model for amr to text generation"

Connected through hyperedge HE-3 (induced by source paper "densely connected graph convolutional networks for graph to sequence learning"):
  with members:
    - "structural neural encoders for amr to text generation"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "representation learning on graphs with jumping knowledge networks"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "graph to sequence learning using gated graph neural networks"
    - "deep contextualized word representations"
    - "a graph to sequence model for amr to text generation"
    - "deep graph convolutional encoders for structured data to text generation"

Connected through hyperedge HE-4 (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"
    - "factorising amr generation through syntax"
    - "toward abstractive summarization using semantic representations"
    - "graph2seq graph to sequence learning with attention based neural networks"

Connected through hyperedge HE-5 (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"
    - "factorising amr generation through syntax"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "graph2seq graph to sequence learning with attention based neural networks"):
  with members:
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "graph to sequence learning using gated graph neural networks"
    - "learning deep generative models of graphs"
    - "graph embedding techniques applications and performance a survey"
    - "fastgcn fast learning with graph convolutional networks via importance sampling"
    - "typesql knowledge based type aware neural text to sql generation"
    - "inductive representation learning on large graphs"

Connected through hyperedge HE-7 (induced by source paper "graph to sequence learning using gated graph neural networks"):
  with members:
    - "learning to parse and translate improves neural machine translation"
    - "sockeye a toolkit for neural machine translation"
    - "attention is all you need"
    - "neural machine translation with source side latent graph parsing"
    - "neural amr sequence to sequence models for parsing and generation"
    - "graph convolutional encoders for syntax aware neural machine translation"
    - "towards string to tree neural machine translation"
    - "reporting score distributions makes a difference performance study of lstm networks for sequence tagging"

Connected through hyperedge HE-8 (induced by source paper "abstract meaning representation for multi document summarization"):
  with members:
    - "toward abstractive summarization using semantic representations"
    - "structure infused copy mechanisms for abstractive summarization"
    - "survey of the state of the art in natural language generation core tasks applications and evaluation"
    - "selective encoding for abstractive sentence summarization"
    - "amr parsing using stack lstms"
    - "get to the point summarization with pointer generator networks"
    - "robust incremental neural semantic graph parsing"
    - "faithful to the original fact aware neural abstractive summarization"

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)