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: "iterative document representation learning towards summarization with polishing"
  Abstract: In this paper, we introduce Iterative Text Summarization (ITS), an iteration-based model for supervised extractive text summarization, inspired by the observation that it is often necessary for a human to read an article multiple times in order to fully understand and summarize its contents. Current summarization approaches read through a document only once to generate a document representation, resulting in a sub-optimal representation. To address this issue we introduce a model which iteratively polishes the document representation on many passes through the document. As part of our model, we also introduce a selective reading mechanism that decides more accurately the extent to which each sentence in the model should be updated. Experimental results on the CNN/DailyMail and DUC2002 datasets demonstrate that our model significantly outperforms state-of-the-art extractive systems when evaluated by machines and by humans.

Connected through hyperedge HE-1 (induced by source paper "hibert document level pre training of hierarchical bidirectional transformers for document summarization"):
  with members:
    - "neural extractive text summarization with syntactic compression"
    - "deep communicating agents for abstractive summarization"
    - "a unified model for extractive and abstractive summarization using inconsistency loss"
    - "bottom up abstractive summarization"
    - "banditsum extractive summarization as a contextual bandit"
    - "deep contextualized word representations"
    - "ranking sentences for extractive summarization with reinforcement learning"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-2 (induced by source paper "how to write summaries with patterns learning towards abstractive summarization through prototype editing"):
  with members:
    - "gsn a graph structured network for multi party dialogues"
    - "abstractive text summarization by incorporating reader comments"
    - "skeleton to response dialogue generation guided by retrieval memory"
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "a unified model for extractive and abstractive summarization using inconsistency loss"
    - "learning to write with cooperative discriminators"
    - "response generation by context aware prototype editing"
    - "abstractive summarization of reddit posts with multi level memory networks"

Connected through hyperedge HE-3 (induced by source paper "abstractive text summarization by incorporating reader comments"):
  with members:
    - "product aware answer generation in e commerce question answering"
    - "global encoding for abstractive summarization"
    - "a unified model for extractive and abstractive summarization using inconsistency loss"
    - "a reinforced topic aware convolutional sequence to sequence model for abstractive text summarization"
    - "ranking sentences for extractive summarization with reinforcement learning"
    - "autoencoder as assistant supervisor improving text representation for chinese social media text summarization"
    - "fast abstractive summarization with reinforce selected sentence rewriting"
    - "selective encoding for abstractive sentence 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)