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: "toward abstractive summarization using semantic representations"
  Abstract: We present a novel abstractive summarization framework that draws on the recent development of a treebank for the Abstract Meaning Representation (AMR). In this framework, the source text is parsed to a set of AMR graphs, the graphs are transformed into a summary graph, and then text is generated from the summary graph. We focus on the graph-to-graph transformation that reduces the source semantic graph into a summary graph, making use of an existing AMR parser and assuming the eventual availability of an AMR-to-text generator. The framework is data-driven, trainable, and not specifically designed for a particular domain. Experiments on gold-standard AMR annotations and system parses show promising results. Code is available at: this https URL

Connected through hyperedge HE-1 (induced by source paper "structsum incorporating latent and explicit sentence dependencies for single document summarization"):
  with members:
    - "evaluating discourse in structured text representations"
    - "unsupervised neural single document summarization of reviews via learning latent discourse structure and its ranking"
    - "text summarization with pretrained encoders"
    - "a unified model for extractive and abstractive summarization using inconsistency loss"
    - "bottom up abstractive summarization"
    - "don t give me the details just the summary topic aware convolutional neural networks for extreme summarization"
    - "structure infused copy mechanisms for abstractive summarization"
    - "a discourse aware attention model for abstractive summarization of long documents"

Connected through hyperedge HE-2 (induced by source paper "joint parsing and generation for abstractive summarization"):
  with members:
    - "analyzing sentence fusion in abstractive summarization"
    - "controlling the amount of verbatim copying in abstractive summarization"
    - "scoring sentence singletons and pairs for abstractive summarization"
    - "deep communicating agents for abstractive summarization"
    - "improving abstraction in text summarization"
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "generating wikipedia by summarizing long sequences"
    - "bottom up abstractive summarization"

Connected through hyperedge HE-3 (induced by source paper "controlling the amount of verbatim copying in abstractive summarization"):
  with members:
    - "sample efficient text summarization using a single pre trained transformer"
    - "biset bi directional selective encoding with template for abstractive summarization"
    - "bertscore evaluating text generation with bert"
    - "unified language model pre training for natural language understanding and generation"
    - "deep communicating agents for abstractive summarization"
    - "improving abstraction in text summarization"
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "bottom up abstractive summarization"

Connected through hyperedge HE-4 (induced by source paper "using local knowledge graph construction to scale seq2seq models to multi document inputs"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "multi news a large scale multi document summarization dataset and abstractive hierarchical model"
    - "transformer xl attentive language models beyond a fixed length context"
    - "lattice cnns for matching based chinese question answering"
    - "adaptive attention span in transformers"
    - "eli5 long form question answering"
    - "graph neural networks a review of methods and applications"
    - "building dynamic knowledge graphs from text using machine reading comprehension"

Connected through hyperedge HE-5 (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"
    - "graph2seq graph to sequence learning with attention based neural networks"
    - "a graph to sequence model for amr to text generation"

Connected through hyperedge HE-6 (induced by source paper "earlier isn t always better sub aspect analysis on corpus and system biases in summarization"):
  with members:
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "content selection in deep learning models of summarization"
    - "bottom up abstractive summarization"
    - "a simple theoretical model of importance for summarization"
    - "ranking sentences for extractive summarization with reinforcement learning"
    - "don t give me the details just the summary topic aware convolutional neural networks for extreme summarization"
    - "a dataset of peer reviews peerread collection insights and nlp applications"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-7 (induced by source paper "core semantic first a top down approach for amr parsing"):
  with members:
    - "compositional semantic parsing across graphbanks"
    - "rewarding smatch transition based amr parsing with reinforcement learning"
    - "amr dependency parsing with a typed semantic algebra"
    - "an amr aligner tuned by transition based parser"
    - "amr parsing as graph prediction with latent alignment"
    - "linguistically informed self attention for semantic role labeling"
    - "guided neural language generation for abstractive summarization using abstract meaning representation"
    - "attention is all you need"

Connected through hyperedge HE-8 (induced by source paper "structural neural encoders for amr to text generation"):
  with members:
    - "graph to sequence learning using gated graph neural networks"
    - "graph2seq graph to sequence learning with attention based neural networks"
    - "a graph to sequence model for amr to text generation"
    - "deep graph convolutional encoders for structured data to text generation"
    - "graph convolutional networks for named entity recognition"
    - "opennmt open source toolkit for neural machine translation"
    - "neural amr sequence to sequence models for parsing and generation"
    - "improved neural machine translation with a syntax aware encoder and decoder"

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)