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: "word embedding attention network generating words by querying distributed word representations for paraphrase generation"
  Abstract: Most recent approaches use the sequence-to-sequence model for paraphrase generation. The existing sequence-to-sequence model tends to memorize the words and the patterns in the training dataset instead of learning the meaning of the words. Therefore, the generated sentences are often grammatically correct but semantically improper. In this work, we introduce a novel model based on the encoder-decoder framework, called Word Embedding Attention Network (WEAN). Our proposed model generates the words by querying distributed word representations (i.e. neural word embeddings), hoping to capturing the meaning of the according words. Following previous work, we evaluate our model on two paraphrase-oriented tasks, namely text simplification and short text abstractive summarization. Experimental results show that our model outperforms the sequence-to-sequence baseline by the BLEU score of 6.3 and 5.5 on two English text simplification datasets, and the ROUGE-2 F1 score of 5.7 on a Chinese summarization dataset. Moreover, our model achieves state-of-the-art performances on these three benchmark datasets.

Connected through hyperedge HE-1 (induced by source paper "a hybrid word character model for abstractive summarization"):
  with members:
    - "actor critic based training framework for abstractive summarization"
    - "deep recurrent generative decoder for abstractive text summarization"
    - "attention is all you need"
    - "opennmt open source toolkit for neural machine translation"
    - "distraction based neural networks for document summarization"
    - "abstractive text summarization using sequence to sequence rnns and beyond"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "neural headline generation with minimum risk training"

Connected through hyperedge HE-2 (induced by source paper "regularizing output distribution of abstractive chinese social media text summarization for improved semantic consistency"):
  with members:
    - "a hierarchical end to end model for jointly improving text summarization and sentiment classification"
    - "decoding history based adaptive control of attention for neural machine translation"
    - "dp gan diversity promoting generative adversarial network for generating informative and diversified text"
    - "get to the point summarization with pointer generator networks"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "softtarget regularization an effective technique to reduce over fitting in neural networks"
    - "supervised attentions for neural machine translation"
    - "a neural attention model for abstractive sentence summarization"

Connected through hyperedge HE-3 (induced by source paper "paraphrase generation with deep reinforcement learning"):
  with members:
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "no metrics are perfect adversarial reward learning for visual storytelling"
    - "a continuously growing dataset of sentential paraphrases"
    - "learning to paraphrase for question answering"
    - "attention is all you need"
    - "paranmt 50m pushing the limits of paraphrastic sentence embeddings with millions of machine translations"
    - "learning paraphrastic sentence embeddings from back translated bitext"
    - "get to the point summarization with pointer generator 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)