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: "neural abstractive text summarization with sequence to sequence models a survey"
  Abstract: In the past few years, neural abstractive text summarization with sequence-to-sequence (seq2seq) models have gained a lot of popularity. Many interesting techniques have been proposed to improve the seq2seq models, making them capable of handling different challenges, such as saliency, fluency and human readability, and generate high-quality summaries. Generally speaking, most of these techniques differ in one of these three categories: network structure, parameter inference, and decoding/generation. There are also other concerns, such as efficiency and parallelism for training a model. In this paper, we provide a comprehensive literature and technical survey on different seq2seq models for abstractive text summarization from viewpoint of network structures, training strategies, and summary generation algorithms. Many models were first proposed for language modeling and generation tasks, such as machine translation, and later applied to abstractive text summarization. Therefore, we also provide a brief review of these models. As part of this survey, we also develop an open source library, namely Neural Abstractive Text Summarizer (NATS) toolkit, for the abstractive text summarization. An extensive set of experiments have been conducted on the widely used CNN/Daily Mail dataset to examine the effectiveness of several different neural network components. Finally, we benchmark two models implemented in NATS on two recently released datasets, i.e., Newsroom and Bytecup.

Connected through hyperedge HE-1 (induced by source paper "deep reinforced self attention masks for abstractive summarization dr sas"):
  with members:
    - "on extractive and abstractive neural document summarization with transformer language models"
    - "learning to extract coherent summary via deep reinforcement learning"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "deep learning for sequential recommendation algorithms influential factors and evaluations"):
  with members:
    - "session based recommendation with graph neural network"
    - "bert4rec sequential recommendation with bidirectional encoder representations from transformer"
    - "learning to recommend with multiple cascading behaviors"
    - "research commentary on recommendations with side information a survey and research directions"
    - "improving end to end sequential recommendations with intent aware diversification"
    - "are we really making much progress a worrying analysis of recent neural recommendation approaches"
    - "a survey on session based recommender systems"
    - "deep learning based recommender system a survey and new perspectives"

Connected through hyperedge HE-3 (induced by source paper "a translate edit model for natural language question to sql query generation on multi relational healthcare data"):
  with members:
    - "leafnats an open source toolkit and live demo system for neural abstractive text summarization"
    - "table2answer read the database and answer without sql"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "sqlnet generating structured queries from natural language without reinforcement learning"
    - "the natural language decathlon multitask learning as question answering"
    - "bottom up abstractive summarization"
    - "improving text to sql evaluation methodology"
    - "translating natural language to sql using pointer generator networks and how decoding order matters"

Connected through hyperedge HE-4 (induced by source paper "efficient adaptation of pretrained transformers for abstractive summarization"):
  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"
    - "abstractive summarization of reddit posts with multi level memory networks"
    - "bottom up abstractive summarization"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"
    - "don t give me the details just the summary topic aware convolutional neural networks for extreme summarization"

Connected through hyperedge HE-5 (induced by source paper "leafnats an open source toolkit and live demo system for neural abstractive text summarization"):
  with members:
    - "deep communicating agents for abstractive summarization"
    - "deep reinforcement learning for sequence to sequence models"
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "texar a modularized versatile and extensible toolkit for text generation"
    - "xnmt the extensible neural machine translation toolkit"
    - "bottom up abstractive summarization"
    - "opennmt open source toolkit for neural machine translation"
    - "get to the point summarization with pointer generator networks"

Connected through hyperedge HE-6 (induced by source paper "deep transfer reinforcement learning for text summarization"):
  with members:
    - "a practitioners guide to transfer learning for text classification using convolutional neural networks"
    - "deep reinforcement learning for sequence to sequence models"
    - "improving abstraction in text summarization"
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "tensor2tensor for neural machine translation"
    - "the natural language decathlon multitask learning as question answering"
    - "bottom up abstractive summarization"
    - "ranking sentences for extractive summarization with reinforcement 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)