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 "ccmatrix mining billions of high quality parallel sentences on the web"):
  Abstract: We show that margin-based bitext mining in a multilingual sentence space can be applied to monolingual corpora of billions of sentences. We are using ten snapshots of a curated common crawl corpus (Wenzek et al., 2019) totaling 32.7 billion unique sentences. Using one unified approach for 38 languages, we were able to mine 3.5 billions parallel sentences, out of which 661 million are aligned with English. 17 language pairs have more then 30 million parallel sentences, 82 more then 10 million, and most more than one million, including direct alignments between many European or Asian languages. #R##N#To evaluate the quality of the mined bitexts, we train NMT systems for most of the language pairs and evaluate them on TED, WMT and WAT test sets. Using our mined bitexts only and no human translated parallel data, we achieve a new state-of-the-art for a single system on the WMT'19 test set for translation between English and German, Russian and Chinese, as well as German/French. In particular, our English/German system outperforms the best single one by close to 4 BLEU points and is almost on pair with best WMT'19 evaluation system which uses system combination and back-translation. We also achieve excellent results for distant languages pairs like Russian/Japanese, outperforming the best submission at the 2019 workshop on Asian Translation (WAT).

Includes members:
  Member "reducing transformer depth on demand with structured dropout":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "rethinking batch normalization in transformers")
  Member "ccnet extracting high quality monolingual datasets from web crawl data":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multilingual denoising pre training for neural machine translation")
  Member "wikimatrix mining 135m parallel sentences in 1620 language pairs from wikipedia":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "pmindia a collection of parallel corpora of languages of india")
  Member "achieving human parity on automatic chinese to english news translation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "gret global representation enhanced transformer")
  Member "effective parallel corpus mining using bilingual sentence embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "contextual lensing of universal sentence representations")
  Member "filtering and mining parallel data in a joint multilingual space":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "contextual lensing of universal sentence representations")
  Member "learning word vectors for 157 languages":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "margin based parallel corpus mining with multilingual sentence embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "contextual lensing of universal sentence representations")

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)