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 "fairseq a fast extensible toolkit for sequence modeling"):
  Abstract: fairseq is an open-source sequence modeling toolkit that allows researchers and developers to train custom models for translation, summarization, language modeling, and other text generation tasks. The toolkit is based on PyTorch and supports distributed training across multiple GPUs and machines. We also support fast mixed-precision training and inference on modern GPUs. A demo video can be found at this https URL

Includes members:
  Member "strategies for structuring story generation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a knowledge enhanced pretraining model for commonsense story generation")
  Member "mixture models for diverse machine translation tricks of the trade":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on the discrepancy between density estimation and sequence generation")
  Member "fast parametric learning with activation memorization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "time aware large kernel convolutions")
  Member "weighted transformer network for machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "meta embeddings based on self attention")
  Member "a stable and effective learning strategy for trainable greedy decoding":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "plug and play language models a simple approach to controlled text generation")
  Member "self attention with relative position representations":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "non autoregressive neural dialogue generation")
  Member "bottom up abstractive summarization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "minilm deep self attention distillation for task agnostic compression of pre trained transformers")
  Member "a multilayer convolutional encoder decoder neural network for grammatical error correction":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "denoising based sequence to sequence pre training for text generation")

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)