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: "text summarization using abstract meaning representation"
  Abstract: With an ever increasing size of text present on the Internet, automatic summary generation remains an important problem for natural language understanding. In this work we explore a novel full-fledged pipeline for text summarization with an intermediate step of Abstract Meaning Representation (AMR). The pipeline proposed by us first generates an AMR graph of an input story, through which it extracts a summary graph and finally, generate summary sentences from this summary graph. Our proposed method achieves state-of-the-art results compared to the other text summarization routines based on AMR. We also point out some significant problems in the existing evaluation methods, which make them unsuitable for evaluating summary quality.

Connected through hyperedge HE-1 (induced by source paper "point less more abstractive summarization with pointer generator networks"):
  with members:
    - "improving abstraction in text summarization"
    - "query focused abstractive summarization incorporating query relevance multi document coverage and summary length constraints into seq2seq models"
    - "structure infused copy mechanisms for abstractive summarization"
    - "robust neural abstractive summarization systems and evaluation against adversarial information"
    - "abstractive summarization using attentive neural techniques"
    - "controlling decoding for more abstractive summaries with copy based networks"
    - "deep recurrent generative decoder for abstractive text summarization"
    - "text summarization techniques a brief survey"

Connected through hyperedge HE-2 (induced by source paper "semeval 2019 task 1 cross lingual semantic parsing with ucca"):
  with members:
    - "hlt suda at semeval 2019 task 1 ucca graph parsing as constituent tree parsing"
    - "toward abstractive summarization using semantic representations"
    - "multitask parsing across semantic representations"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "simple and effective text simplification using semantic and neural methods"
    - "a transition based directed acyclic graph parser for ucca"
    - "a minimal span based neural constituency parser"

Connected through hyperedge HE-3 (induced by source paper "automatic accuracy prediction for amr parsing"):
  with members:
    - "toward abstractive summarization using semantic representations"
    - "amr dependency parsing with a typed semantic algebra"
    - "amr parsing as graph prediction with latent alignment"
    - "neural semantic parsing by character based translation experiments with abstract meaning representations"
    - "neural amr sequence to sequence models for parsing and generation"
    - "the meaning factory at semeval 2017 task 9 producing amrs with neural semantic parsing"
    - "an incremental parser for abstract meaning representation"
    - "riga at semeval 2016 task 8 impact of smatch extensions and character level neural translation on amr parsing accuracy"

Connected through hyperedge HE-4 (induced by source paper "soft layer specific multi task summarization with entailment and question generation"):
  with members:
    - "toward abstractive summarization using semantic representations"
    - "learning to ask neural question generation for reading comprehension"
    - "get to the point summarization with pointer generator networks"
    - "bringing structure into summaries crowdsourcing a benchmark corpus of concept maps"
    - "one model to learn them all"
    - "what do neural machine translation models learn about morphology"
    - "a deep reinforced model for abstractive summarization"
    - "cross stitch networks for multi task learning"

Connected through hyperedge HE-5 (induced by source paper "multi reward reinforced summarization with saliency and entailment"):
  with members:
    - "toward abstractive summarization using semantic representations"
    - "neural models for key phrase detection and question generation"
    - "reinforced video captioning with entailment rewards"
    - "get to the point summarization with pointer generator networks"
    - "a broad coverage challenge corpus for sentence understanding through inference"
    - "a deep reinforced model for abstractive summarization"
    - "abstractive text summarization using sequence to sequence rnns and beyond"
    - "a decomposable attention model for natural language inference"

Connected through hyperedge HE-6 (induced by source paper "amr parsing as graph prediction with latent alignment"):
  with members:
    - "toward abstractive summarization using semantic representations"
    - "a simple and accurate syntax agnostic neural model for dependency based semantic role labeling"
    - "neural semantic parsing by character based translation experiments with abstract meaning representations"
    - "addressing the data sparsity issue in neural amr parsing"
    - "encoding sentences with graph convolutional networks for semantic role labeling"
    - "language to logical form with neural attention"
    - "learning executable semantic parsers for natural language understanding"
    - "an incremental parser for abstract meaning representation"

Connected through hyperedge HE-7 (induced by source paper "semeval 2019 shared task cross lingual semantic parsing with ucca call for participation"):
  with members:
    - "toward abstractive summarization using semantic representations"
    - "multitask parsing across semantic representations"
    - "the parallel meaning bank towards a multilingual corpus of translations annotated with compositional meaning representations"

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)