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: "searching for effective neural extractive summarization what works and what s next"
  Abstract: The recent years have seen remarkable success in the use of deep neural networks on text summarization. #R##N#However, there is no clear understanding of \textit{why} they perform so well, or \textit{how} they might be improved. #R##N#In this paper, we seek to better understand how neural extractive summarization systems could benefit from different types of model architectures, transferable knowledge and #R##N#learning schemas. Additionally, we find an effective way to improve current frameworks and achieve the state-of-the-art result on CNN/DailyMail by a large margin based on our #R##N#observations and analyses. Hopefully, our work could provide more clues for future research on extractive summarization.

Connected through hyperedge HE-1 (induced by source paper "pegasus pre training with extracted gap sentences for abstractive summarization"):
  with members:
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "bigpatent a large scale dataset for abstractive and coherent summarization"
    - "multi news a large scale multi document summarization dataset and abstractive hierarchical model"
    - "leafnats an open source toolkit and live demo system for neural abstractive text summarization"
    - "sample efficient text summarization using a single pre trained transformer"
    - "billsum a corpus for automatic summarization of us legislation"
    - "spanbert improving pre training by representing and predicting spans"
    - "mass masked sequence to sequence pre training for language generation"

Connected through hyperedge HE-2 (induced by source paper "discourse aware neural extractive model for text summarization"):
  with members:
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "fine tune bert for extractive summarization"
    - "editnts an neural programmer interpreter model for sentence simplification through explicit editing"
    - "an entity driven framework for abstractive summarization"
    - "neural extractive text summarization with syntactic compression"
    - "hibert document level pre training of hierarchical bidirectional transformers for document summarization"
    - "deep communicating agents for abstractive summarization"
    - "content selection in deep learning models of summarization"

Connected through hyperedge HE-3 (induced by source paper "exploring domain shift in extractive text summarization"):
  with members:
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "content selection in deep learning models of summarization"
    - "bottom up abstractive summarization"
    - "a reinforced topic aware convolutional sequence to sequence model for abstractive text summarization"
    - "learning to extract coherent summary via deep reinforcement learning"
    - "banditsum extractive summarization as a contextual bandit"
    - "deep contextualized word representations"
    - "ranking sentences for extractive summarization with reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "a closer look at data bias in neural extractive summarization models"):
  with members:
    - "fine tune bert for extractive summarization"
    - "earlier isn t always better sub aspect analysis on corpus and system biases in summarization"
    - "exploring domain shift in extractive text summarization"
    - "unified language model pre training for natural language understanding and generation"
    - "deep communicating agents for abstractive summarization"
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "bottom up abstractive summarization"
    - "learning to extract coherent summary via deep reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "a graph based model for joint chinese word segmentation and dependency parsing"):
  with members:
    - "utilizing bert for aspect based sentiment analysis via constructing auxiliary sentence"
    - "pre training with whole word masking for chinese bert"
    - "state of the art chinese word segmentation with bi lstms"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "character based joint segmentation and pos tagging for chinese using bidirectional rnn crf"
    - "fast er exact decoding and global training for transition based dependency parsing via a minimal feature set"
    - "decoupled weight decay regularization"
    - "joint pos tagging and dependency parsing with transition based neural networks"

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)