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: "distraction based neural networks for document summarization"
  Abstract: Distributed representation learned with neural networks has recently shown to be effective in modeling natural languages at fine granularities such as words, phrases, and even sentences. Whether and how such an approach can be extended to help model larger spans of text, e.g., documents, is intriguing, and further investigation would still be desirable. This paper aims to enhance neural network models for such a purpose. A typical problem of document-level modeling is automatic summarization, which aims to model documents in order to generate summaries. In this paper, we propose neural models to train computers not just to pay attention to specific regions and content of input documents with attention models, but also distract them to traverse between different content of a document so as to better grasp the overall meaning for summarization. Without engineering any features, we train the models on two large datasets. The models achieve the state-of-the-art performance, and they significantly benefit from the distraction modeling, particularly when input documents are long.

Connected through hyperedge HE-1 (induced by source paper "guiding extractive summarization with question answering rewards"):
  with members:
    - "deep communicating agents for abstractive summarization"
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "content selection in deep learning models of summarization"
    - "bottom up abstractive summarization"
    - "toward extractive summarization of online forum discussions via hierarchical attention networks"
    - "learning to extract coherent summary via deep reinforcement learning"
    - "ranking sentences for extractive summarization with reinforcement learning"
    - "soft layer specific multi task summarization with entailment and question generation"

Connected through hyperedge HE-2 (induced by source paper "in conclusion not repetition comprehensive abstractive summarization with diversified attention based on determinantal point processes"):
  with members:
    - "bigpatent a large scale dataset for abstractive and coherent summarization"
    - "representation degeneration problem in training natural language generation models"
    - "improving abstraction in text summarization"
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "abstractive summarization of reddit posts with multi level memory networks"
    - "bottom up abstractive summarization"
    - "wikihow a large scale text summarization dataset"
    - "fast abstractive summarization with reinforce selected sentence rewriting"

Connected through hyperedge HE-3 (induced by source paper "multi stage pretraining for abstractive summarization"):
  with members:
    - "the curious case of neural text degeneration"
    - "pretraining based natural language generation for text summarization"
    - "neural text generation with unlikelihood training"
    - "leveraging pre trained checkpoints for sequence generation tasks"
    - "rethinking imagenet pre training"
    - "a unified model for extractive and abstractive summarization using inconsistency loss"
    - "bottom up abstractive summarization"
    - "entity commonsense representation for neural abstractive summarization"

Connected through hyperedge HE-4 (induced by source paper "scoring sentence singletons and pairs for abstractive summarization"):
  with members:
    - "deep communicating agents for abstractive summarization"
    - "content selection in deep learning models of summarization"
    - "a unified model for extractive and abstractive summarization using inconsistency loss"
    - "bottom up abstractive summarization"
    - "soft layer specific multi task summarization with entailment and question generation"
    - "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"
    - "structure infused copy mechanisms for abstractive summarization"

Connected through hyperedge HE-5 (induced by source paper "multi modal generative adversarial network for short product title generation in mobile e commerce"):
  with members:
    - "a multi task learning approach for improving product title compression with user search log data"
    - "automatic generation of chinese short product titles for mobile display"
    - "improving automatic source code summarization via deep reinforcement learning"
    - "get to the point summarization with pointer generator networks"
    - "adversarial learning for neural dialogue generation"
    - "adversarial ranking for language generation"
    - "long text generation via adversarial training with leaked information"
    - "towards diverse and natural image descriptions via a conditional gan"

Connected through hyperedge HE-6 (induced by source paper "ranking sentences from product description bullets for better search"):
  with members:
    - "ranking sentences for extractive summarization with reinforcement learning"
    - "towards a simplified ontology for better e commerce search"
    - "text summarization techniques a brief survey"
    - "get to the point summarization with pointer generator networks"
    - "graph based neural multi document summarization"
    - "neural extractive summarization with side information"
    - "a deep reinforced model for abstractive summarization"
    - "deep reinforcement learning for dialogue generation"

Connected through hyperedge HE-7 (induced by source paper "a novel ilp framework for summarizing content with high lexical variety"):
  with members:
    - "deep communicating agents for abstractive summarization"
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "scientific document summarization via citation contextualization and scientific discourse"
    - "toward extractive summarization of online forum discussions via hierarchical attention networks"
    - "ranking sentences for extractive summarization with reinforcement learning"
    - "soft layer specific multi task summarization with entailment and question generation"
    - "automatic summarization of student course feedback"
    - "an improved phrase based approach to annotating and summarizing student course responses"

Connected through hyperedge HE-8 (induced by source paper "adapting the neural encoder decoder framework from single to multi document summarization"):
  with members:
    - "improving abstraction in text summarization"
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "bottom up abstractive summarization"
    - "toward abstractive summarization using semantic representations"
    - "ranking sentences for extractive summarization with reinforcement learning"
    - "automatic summarization of student course feedback"
    - "query focused abstractive summarization incorporating query relevance multi document coverage and summary length constraints into seq2seq models"
    - "structure infused copy mechanisms for abstractive summarization"

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)