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 "ted a pretrained unsupervised summarization model with theme modeling and denoising"):
  Abstract: Text summarization aims to extract essential information from a piece of text and transform it into a concise version. Existing unsupervised abstractive summarization models use recurrent neural networks framework and ignore abundant unlabeled corpora resources. In order to address these issues, we propose TED, a transformer-based unsupervised summarization system with pretraining on large-scale data. We first leverage the lead bias in news articles to pretrain the model on large-scale corpora. Then, we finetune TED on target domains through theme modeling and a denoising autoencoder to enhance the quality of summaries. Notably, TED outperforms all unsupervised abstractive baselines on NYT, CNN/DM and English Gigaword datasets with various document styles. Further analysis shows that the summaries generated by TED are abstractive and containing even higher proportions of novel tokens than those from supervised models.

Includes members:
  Member "seq 3 differentiable sequence to sequence to sequence autoencoder for unsupervised abstractive sentence compression":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "summae zero shot abstractive text summarization using length agnostic auto encoders")
  Member "make lead bias in your favor a simple and effective method for news summarization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "boosting factual correctness of abstractive summarization with knowledge graph")
  Member "on the variance of the adaptive learning rate and beyond":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "sentence centrality revisited for unsupervised summarization":
    also connected through other hyperedges:
      (none)
  Member "text summarization with pretrained encoders":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "bert can see out of the box on the cross modal transferability of text representations")
  Member "unsupervised text style transfer using language models as discriminators":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "blank language models")
  Member "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "multilingual denoising pre training for neural machine translation")
  Member "unsupervised sentence compression using denoising auto encoders":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning autocomplete systems as a communication game")

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)