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: "autoencoder as assistant supervisor improving text representation for chinese social media text summarization"
  Abstract: Most of the current abstractive text summarization models are based on the sequence-to-sequence model (Seq2Seq). The source content of social media is long and noisy, so it is difficult for Seq2Seq to learn an accurate semantic representation. Compared with the source content, the annotated summary is short and well written. Moreover, it shares the same meaning as the source content. In this work, we supervise the learning of the representation of the source content with that of the summary. In implementation, we regard a summary autoencoder as an assistant supervisor of Seq2Seq. Following previous work, we evaluate our model on a popular Chinese social media dataset. Experimental results show that our model achieves the state-of-the-art performances on the benchmark dataset.

Connected through hyperedge HE-1 (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-2 (induced by source paper "dp gan diversity promoting generative adversarial network for generating informative and diversified text"):
  with members:
    - "global encoding for abstractive summarization"
    - "bag of words as target for neural machine translation"
    - "generating sentences by editing prototypes"
    - "began boundary equilibrium generative adversarial networks"
    - "generating high quality and informative conversation responses with sequence to sequence models"
    - "adversarial learning for neural dialogue generation"
    - "improving semantic relevance for sequence to sequence learning of chinese social media text summarization"
    - "improved training of wasserstein gans"

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"
    - "iterative document representation learning towards summarization with polishing"
    - "ranking sentences for extractive summarization with reinforcement learning"
    - "fast abstractive summarization with reinforce selected sentence rewriting"
    - "selective encoding for abstractive sentence summarization"

Connected through hyperedge HE-4 (induced by source paper "a skeleton based model for promoting coherence among sentences in narrative story generation"):
  with members:
    - "hierarchical neural story generation"
    - "bag of words as target for neural machine translation"
    - "event representations for automated story generation with deep neural nets"
    - "faithful to the original fact aware neural abstractive summarization"
    - "table to text generation by structure aware seq2seq learning"
    - "deep reinforcement learning for dialogue generation"
    - "a hierarchical neural autoencoder for paragraphs and documents"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-5 (induced by source paper "learning sentiment memories for sentiment modification without parallel data"):
  with members:
    - "global encoding for abstractive summarization"
    - "unpaired sentiment to sentiment translation a cycled reinforcement learning approach"
    - "style transfer from non parallel text by cross alignment"
    - "structured attention networks"
    - "style transfer in text exploration and evaluation"
    - "table to text generation by structure aware seq2seq learning"
    - "end to end memory networks"
    - "adam a method for stochastic optimization"

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)