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 as a traveling salesman problem"
  Abstract: The task of AMR-to-text generation is to generate grammatical text that sustains the semantic meaning for a given AMR graph. We at- tack the task by first partitioning the AMR graph into smaller fragments, and then generating the translation for each fragment, before finally deciding the order by solving an asymmetric generalized traveling salesman problem (AGTSP). A Maximum Entropy classifier is trained to estimate the traveling costs, and a TSP solver is used to find the optimized solution. The final model reports a BLEU score of 22.44 on the SemEval-2016 Task8 dataset.

Connected through hyperedge HE-1 (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-2 (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-3 (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-4 (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-5 (induced by source paper "easy to hard leveraging simple questions for complex question generation"):
  with members:
    - "multimodal differential network for visual question generation"
    - "learning to ask questions in open domain conversational systems with typed decoders"
    - "the web as a knowledge base for answering complex questions"
    - "toward abstractive summarization using semantic representations"
    - "zero shot question generation from knowledge graphs for unseen predicates and entity types"
    - "learning to ask neural question generation for reading comprehension"
    - "get to the point summarization with pointer generator networks"
    - "creativity generating diverse questions using variational autoencoders"

Connected through hyperedge HE-6 (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"
    - "amr to text generation with synchronous node replacement grammar"
    - "robust incremental neural semantic graph parsing"

Connected through hyperedge HE-7 (induced by source paper "factorising amr generation through syntax"):
  with members:
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "multi source syntactic neural machine translation"
    - "graph to sequence learning using gated graph neural networks"
    - "a graph to sequence model for amr to text generation"
    - "structvae tree structured latent variable models for semi supervised semantic parsing"
    - "neural amr sequence to sequence models for parsing and generation"
    - "get to the point summarization with pointer generator networks"
    - "latent variable dialogue models and their diversity"

Connected through hyperedge HE-8 (induced by source paper "guided neural language generation for abstractive summarization using abstract meaning representation"):
  with members:
    - "toward abstractive summarization using semantic representations"
    - "neural semantic parsing by character based translation experiments with abstract meaning representations"
    - "get to the point summarization with pointer generator networks"
    - "challenges in data to document generation"
    - "a neural attention model for abstractive sentence summarization"
    - "effective approaches to attention based neural machine translation"
    - "teaching machines to read and comprehend"

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)