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 "context aware monolingual repair for neural machine translation"):
  Abstract: Modern sentence-level NMT systems often produce plausible translations of isolated sentences. However, when put in context, these translations may end up being inconsistent with each other. We propose a monolingual DocRepair model to correct inconsistencies between sentence-level translations. DocRepair performs automatic post-editing on a sequence of sentence-level translations, refining translations of sentences in context of each other. For training, the DocRepair model requires only monolingual document-level data in the target language. It is trained as a monolingual sequence-to-sequence model that maps inconsistent groups of sentences into consistent ones. The consistent groups come from the original training data; the inconsistent groups are obtained by sampling round-trip translations for each isolated sentence. We show that this approach successfully imitates inconsistencies we aim to fix: using contrastive evaluation, we show large improvements in the translation of several contextual phenomena in an English-Russian translation task, as well as improvements in the BLEU score. We also conduct a human evaluation and show a strong preference of the annotators to corrected translations over the baseline ones. Moreover, we analyze which discourse phenomena are hard to capture using monolingual data only.

Includes members:
  Member "ape at scale and its implications on mt evaluation biases":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "domain translationese and noise in synthetic data for 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-2 (induced by source paper "capturing document context inside sentence level neural machine translation models with self training")
  Member "achieving human parity on automatic chinese to english news translation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "gret global representation enhanced transformer")
  Member "modeling coherence for discourse neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "capturing document context inside sentence level neural machine translation models with self training")
  Member "training tips for the transformer model":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "disentangling adaptive gradient methods from learning rates")
  Member "context aware neural machine translation learns anaphora resolution":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "toward making the most of context in neural machine translation")
  Member "modeling coherence for neural machine translation with dynamic and topic caches":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "capturing document context inside sentence level neural machine translation models with self training")
  Member "neural machine translation with extended context":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "multilingual denoising pre training for 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)