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 "capturing document context inside sentence level neural machine translation models with self training"):
  Abstract: Neural machine translation (NMT) has arguably achieved human level parity when trained and evaluated at the sentence-level. Document-level neural machine translation has received less attention and lags behind its sentence-level counterpart. The majority of the proposed document-level approaches investigate ways of conditioning the model on several source or target sentences to capture document context. These approaches require training a specialized NMT model from scratch on parallel document-level corpora. We propose an approach that doesn't require training a specialized model on parallel document-level corpora and is applied to a trained sentence-level NMT model at decoding time. We process the document from left to right multiple times and self-train the sentence-level model on pairs of source sentences and generated translations. Our approach reinforces the choices made by the model, thus making it more likely that the same choices will be made in other sentences in the document. We evaluate our approach on three document-level datasets: NIST Chinese-English, WMT'19 Chinese-English and OpenSubtitles English-Russian. We demonstrate that our approach has higher BLEU score and higher human preference than the baseline. Qualitative analysis of our approach shows that choices made by model are consistent across the document.

Includes members:
  Member "putting machine translation in context with the noisy channel model":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "modelling latent skills for multitask language generation")
  Member "microsoft translator at wmt 2019 towards large scale document level neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "toward making the most of context in neural machine translation")
  Member "context aware monolingual repair for neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "toward making the most of context in neural machine translation")
  Member "when a good translation is wrong in context context aware machine translation improves on deixis ellipsis and lexical cohesion":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a survey on document level machine translation methods and evaluation")
  Member "revisiting self training for neural sequence generation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "unsupervised pidgin text generation by pivoting english data and self training")
  Member "fill in the blanks imputing missing sentences for larger context neural machine translation":
    also connected through other hyperedges:
      (none)
  Member "achieving human parity on automatic chinese to english news translation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "gret global representation enhanced transformer")
  Member "modeling coherence for discourse neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "toward making the most of context in neural machine translation")

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)