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: "efficient summarization with read again and copy mechanism"
  Abstract: Encoder-decoder models have been widely used to solve sequence to sequence prediction tasks. However current approaches suffer from two shortcomings. First, the encoders compute a representation of each word taking into account only the history of the words it has read so far, yielding suboptimal representations. Second, current models utilize large vocabularies in order to minimize the problem of unknown words, resulting in slow decoding times and large storage costs. In this paper we address both shortcomings. Towards this goal, we first introduce a simple mechanism that first reads the input sequence before committing to a representation of each word. Furthermore, we propose a simple copy mechanism that is able to exploit very small vocabularies and handle out-of-vocabulary words. We demonstrate the effectiveness of our approach on the Gigaword dataset and DUC competition outperforming the state-of-the-art.

Connected through hyperedge HE-1 (induced by source paper "summary level training of sentence rewriting for abstractive summarization"):
  with members:
    - "fine tune bert for extractive summarization"
    - "efficient adaptation of pretrained transformers for abstractive summarization"
    - "bertscore evaluating text generation with bert"
    - "pretraining based natural language generation for text summarization"
    - "bert has a mouth and it must speak bert as a markov random field language model"
    - "neural extractive text summarization with syntactic compression"
    - "hibert document level pre training of hierarchical bidirectional transformers for document summarization"
    - "xlnet generalized autoregressive pretraining for language understanding"

Connected through hyperedge HE-2 (induced by source paper "improving abstractive text summarization with history aggregation"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "hibert document level pre training of hierarchical bidirectional transformers for document summarization"
    - "text summarization with pretrained encoders"
    - "scoring sentence singletons and pairs for abstractive summarization"
    - "deep communicating agents for abstractive summarization"
    - "global encoding for abstractive summarization"
    - "a unified model for extractive and abstractive summarization using inconsistency loss"
    - "bottom up abstractive summarization"

Connected through hyperedge HE-3 (induced by source paper "repurposing decoder transformer language models for abstractive summarization"):
  with members:
    - "the curious case of neural text degeneration"
    - "deep communicating agents for abstractive summarization"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "generating wikipedia by summarizing long sequences"
    - "bottom up abstractive summarization"
    - "deep contextualized word representations"
    - "don t give me the details just the summary topic aware convolutional neural networks for extreme summarization"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-4 (induced by source paper "context dependent semantic parsing over temporally structured data"):
  with members:
    - "learning to map context dependent sentences to executable formal queries"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "a deep reinforced model for abstractive summarization"
    - "pointing the unknown words"
    - "sequence to sequence learning as beam search optimization"
    - "language to logical form with neural attention"
    - "learning executable semantic parsers for natural language understanding"
    - "incorporating copying mechanism in sequence to sequence learning"

Connected through hyperedge HE-5 (induced by source paper "topic augmented generator for abstractive summarization"):
  with members:
    - "topic guided variational autoencoders for text generation"
    - "learning to encode text as human readable summaries using generative adversarial networks"
    - "wikihow a large scale text summarization dataset"
    - "generative adversarial network for abstractive text summarization"
    - "get to the point summarization with pointer generator networks"
    - "a deep reinforced model for abstractive summarization"
    - "abstractive text summarization using sequence to sequence rnns and beyond"
    - "modeling coverage for neural machine translation"

Connected through hyperedge HE-6 (induced by source paper "bottom up abstractive summarization"):
  with members:
    - "deep communicating agents for abstractive summarization"
    - "generating wikipedia by summarizing long sequences"
    - "actor critic based training framework for abstractive summarization"
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"
    - "a discourse aware attention model for abstractive summarization of long documents"
    - "neural document summarization by jointly learning to score and select sentences"
    - "fast abstractive summarization with reinforce selected sentence rewriting"

Connected through hyperedge HE-7 (induced by source paper "deep communicating agents for abstractive summarization"):
  with members:
    - "reinforced video captioning with entailment rewards"
    - "counterfactual multi agent policy gradients"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "starcraft ii a new challenge for reinforcement learning"
    - "emergence of grounded compositional language in multi agent populations"
    - "controllable abstractive summarization"
    - "a deep reinforced model for abstractive summarization"
    - "neural summarization by extracting sentences and words"

Connected through hyperedge HE-8 (induced by source paper "closed book training to improve summarization encoder memory"):
  with members:
    - "improved image captioning via policy gradient optimization of spider"
    - "get to the point summarization with pointer generator networks"
    - "multi channel encoder for neural machine translation"
    - "a deep reinforced model for abstractive summarization"
    - "pointing the unknown words"
    - "memory enhanced decoder for neural machine translation"
    - "abstractive text summarization using sequence to sequence rnns and beyond"
    - "incorporating copying mechanism in sequence to sequence learning"

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)