human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "distilling the knowledge of bert for text generation"):
  Abstract: Large-scale pre-trained language model, such as BERT, has recently achieved great success in a wide range of language understanding tasks. However, it remains an open question how to utilize BERT for text generation tasks. In this paper, we present a novel approach to addressing this challenge in a generic sequence-to-sequence (Seq2Seq) setting. We first propose a new task, Conditional Masked Language Modeling (C-MLM), to enable fine-tuning of BERT on target text-generation dataset. The fine-tuned BERT (i.e., teacher) is then exploited as extra supervision to improve conventional Seq2Seq models (i.e., student) for text generation. By leveraging BERT's idiosyncratic bidirectional nature, distilling the knowledge learned from BERT can encourage auto-regressive Seq2Seq models to plan ahead, imposing global sequence-level supervision for coherent text generation. Experiments show that the proposed approach significantly outperforms strong baselines of Transformer on multiple text generation tasks, including machine translation (MT) and text summarization. Our proposed model also achieves new state-of-the-art results on the IWSLT German-English and English-Vietnamese MT datasets.

Includes members:
  Member "pay less attention with lightweight and dynamic convolutions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on feature normalization and data augmentation")
  Member "towards making the most of bert in neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "incorporating bert into neural machine translation")
  Member "mask predict parallel decoding of conditional masked language models":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "blank language models")
  Member "multilingual neural machine translation with knowledge distillation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a brief survey of multilingual neural machine translation")
  Member "mass masked sequence to sequence pre training for language generation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a survey on contextual embeddings")
  Member "pretraining based natural language generation for text summarization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "generating representative headlines for news stories")
  Member "bert has a mouth and it must speak bert as a markov random field language model":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "bert as a teacher contextual embeddings for sequence level reward")
  Member "patient knowledge distillation for bert model compression":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a survey on contextual embeddings")

Question: Please tell me which class the source paper 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)