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: "classify or select neural architectures for extractive document summarization"
  Abstract: We present two novel and contrasting Recurrent Neural Network (RNN) based architectures for extractive summarization of documents. The Classifier based architecture sequentially accepts or rejects each sentence in the original document order for its membership in the summary. The Selector architecture, on the other hand, is free to pick one sentence at a time in any arbitrary order to generate the extractive summary. #N##N#Our models under both architectures jointly capture the notions of salience and redundancy of sentences. In addition, these models have the advantage of being very interpretable, since they allow visualization of their predictions broken up by abstract features such as information content, salience and redundancy. #N##N#We show that our models reach or outperform state-of-the-art supervised models on two different corpora. We also recommend the conditions under which one architecture is superior to the other based on experimental evidence.

Connected through hyperedge HE-1 (induced by source paper "neural text summarization a critical evaluation"):
  with members:
    - "what comes next extractive summarization by next sentence prediction"
    - "neural extractive text summarization with syntactic compression"
    - "improving abstraction in text summarization"
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "content selection in deep learning models of summarization"
    - "a unified model for extractive and abstractive summarization using inconsistency loss"
    - "annotation artifacts in natural language inference data"
    - "generating wikipedia by summarizing long sequences"

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 "on extractive and abstractive neural document summarization with transformer language models"):
  with members:
    - "bigpatent a large scale dataset for abstractive and coherent summarization"
    - "jointly extracting and compressing documents with summary state representations"
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "bottom up abstractive summarization"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a discourse aware attention model for abstractive summarization of long documents"
    - "controlling decoding for more abstractive summaries with copy based networks"
    - "fast abstractive summarization with reinforce selected sentence rewriting"

Connected through hyperedge HE-4 (induced by source paper "multi news a large scale multi document summarization dataset and abstractive hierarchical model"):
  with members:
    - "deep communicating agents for abstractive summarization"
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "generating wikipedia by summarizing long sequences"
    - "bottom up abstractive 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"
    - "query focused abstractive summarization incorporating query relevance multi document coverage and summary length constraints into seq2seq models"
    - "adapting the neural encoder decoder framework from single to multi document summarization"

Connected through hyperedge HE-5 (induced by source paper "jointly extracting and compressing documents with summary state representations"):
  with members:
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "a unified model for extractive and abstractive summarization using inconsistency loss"
    - "bottom up abstractive 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"
    - "neural latent extractive document summarization"
    - "fast abstractive summarization with reinforce selected sentence rewriting"
    - "get to the point summarization with pointer generator networks"

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 "ensemble sequence level training for multimodal mt osu baidu wmt18 multimodal machine translation system report"):
  with members:
    - "multi reference training with pseudo references for neural translation and text generation"
    - "improved image captioning via policy gradient optimization of spider"
    - "neural system combination for machine translation"
    - "findings of the second shared task on multimodal machine translation and multilingual image description"
    - "osu multimodal machine translation system report"
    - "multi30k multilingual english german image descriptions"
    - "self critical sequence training for image captioning"
    - "a neural attention model for abstractive sentence summarization"

Connected through hyperedge HE-8 (induced by source paper "multi reference training with pseudo references for neural translation and text generation"):
  with members:
    - "attention is all you need"
    - "convolutional sequence to sequence learning"
    - "a neural attention model for abstractive sentence summarization"
    - "deep residual learning for image recognition"
    - "neural machine translation of rare words with subword units"
    - "sequence to sequence video to text"
    - "cider consensus based image description evaluation"
    - "neural machine translation by jointly learning to align and translate"

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)