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 "character aware decoder for neural machine translation"):
  Abstract: Standard neural machine translation (NMT) systems operate primarily on words, ignoring lower-level patterns of morphology. We present a character-aware decoder for NMT that can simultaneously work with both word-level and subword-level sequences which is designed to capture such patterns. We achieve character-awareness by augmenting both the softmax and embedding layers of an attention-based encoder-decoder network with convolutional neural networks that operate on spelling of a word (or subword). While character-aware embeddings have been successfully used in the source-side, we find that mixing character-aware embeddings with standard embeddings is crucial in the target-side. Furthermore, we show that a simple approximate softmax layer can be used for large target-side vocabularies which would otherwise require prohibitively large memory. We experiment on the TED multi-target dataset, translating English into 14 typologically diverse languages. We find that in this low-resource setting, the character-aware decoder provides consistent improvements over word-level and subword-level counterparts with BLEU score gains of up to +3.37.

Includes members:
  Member "revisiting character based neural machine translation with capacity and compression":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "revisiting low resource neural machine translation a case study")
  Member "bpe and charcnns for translation of morphology a cross lingual comparison and analysis":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on the linguistic representational power of neural machine translation models")
  Member "from characters to words to in between do we capture morphology":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "opennmt open source toolkit for neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "prophetnet predicting future n gram for sequence to sequence pre training")
  Member "document context neural machine translation with memory networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "capturing document context inside sentence level neural machine translation models with self training")
  Member "what do neural machine translation models learn about morphology":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "bi decoder augmented network for neural machine translation")
  Member "character based neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on the linguistic representational power of neural machine translation models")
  Member "gated word character recurrent language model":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "gating mechanisms for combining character and word level word representations an empirical study")

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)