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: "generating news headlines with recurrent neural networks"
  Abstract: We describe an application of an encoder-decoder recurrent neural network with LSTM units and attention to generating headlines from the text of news articles. We find that the model is quite effective at concisely paraphrasing news articles. Furthermore, we study how the neural network decides which input words to pay attention to, and specifically we identify the function of the different neurons in a simplified attention mechanism. Interestingly, our simplified attention mechanism performs better that the more complex attention mechanism on a held out set of articles.

Connected through hyperedge HE-1 (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-2 (induced by source paper "this email could save your life introducing the task of email subject line generation"):
  with members:
    - "deep reinforcement learning for sequence to sequence models"
    - "a unified model for extractive and abstractive summarization using inconsistency loss"
    - "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"
    - "metric for automatic machine translation evaluation based on universal sentence representations"
    - "fast abstractive summarization with reinforce selected sentence rewriting"
    - "sentence simplification with deep reinforcement learning"
    - "actionable email intent modeling with reparametrized rnns"

Connected through hyperedge HE-3 (induced by source paper "outline generation understanding the inherent content structure of documents"):
  with members:
    - "keyphrase generation with correlation constraints"
    - "deep keyphrase generation"
    - "automated phrase mining from massive text corpora"
    - "get to the point summarization with pointer generator networks"
    - "abstractive text summarization using sequence to sequence rnns and beyond"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "sequence to sequence rnns for text summarization"
    - "a neural attention model for abstractive sentence summarization"

Connected through hyperedge HE-4 (induced by source paper "headline generation learning from decomposed document titles"):
  with members:
    - "neural abstractive text summarization and fake news detection"
    - "a novel repetition normalized adversarial reward for headline generation"
    - "abstractive and extractive text summarization using document context vector and recurrent neural networks"
    - "von mises fisher loss for training sequence to sequence models with continuous outputs"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "text summarization techniques a brief survey"
    - "get to the point summarization with pointer generator networks"
    - "source side prediction for neural headline generation"

Connected through hyperedge HE-5 (induced by source paper "unfolding the structure of a document using deep learning"):
  with members:
    - "understanding and representing the semantics of large structured documents"
    - "understanding the logical and semantic structure of large documents"
    - "contextual lstm clstm models for large scale nlp tasks"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "text understanding from scratch"
    - "convolutional neural networks for sentence classification"
    - "sequence to sequence learning with neural networks"
    - "modeling documents with deep boltzmann machines"

Connected through hyperedge HE-6 (induced by source paper "learning to determine the quality of news headlines"):
  with members:
    - "using neural network for identifying clickbaits in online news media"
    - "universal sentence encoder"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "text matching as image recognition"
    - "breaking the news first impressions matter on online news"
    - "distributed representations of sentences and documents"

Connected through hyperedge HE-7 (induced by source paper "abstractive summarization improved by wordnet based extractive sentences"):
  with members:
    - "selective encoding for abstractive sentence summarization"
    - "efficient summarization with read again and copy mechanism"
    - "diversity driven attention model for query based abstractive summarization"
    - "get to the point summarization with pointer generator networks"
    - "improving semantic relevance for sequence to sequence learning of chinese social media text summarization"
    - "a deep reinforced model for abstractive summarization"
    - "pointing the unknown words"
    - "abstractive text summarization using sequence to sequence rnns and beyond"

Connected through hyperedge HE-8 (induced by source paper "abstractive and extractive text summarization using document context vector and recurrent neural networks"):
  with members:
    - "topic based evaluation for conversational bots"
    - "query focused abstractive summarization incorporating query relevance multi document coverage and summary length constraints into seq2seq models"
    - "conversational ai the science behind the alexa prize"
    - "deep recurrent generative decoder for abstractive text summarization"
    - "convolutional sequence to sequence learning"
    - "a deep reinforced model for abstractive summarization"
    - "sequence to sequence learning as beam search optimization"
    - "neural summarization by extracting sentences and words"

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)