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 "korean to chinese machine translation using chinese character as pivot clue"):
  Abstract: Korean-Chinese is a low resource language pair, but Korean and Chinese have a lot in common in terms of vocabulary. Sino-Korean words, which can be converted into corresponding Chinese characters, account for more than fifty of the entire Korean vocabulary. Motivated by this, we propose a simple linguistically motivated solution to improve the performance of the Korean-to-Chinese neural machine translation model by using their common vocabulary. We adopt Chinese characters as a translation pivot by converting Sino-Korean words in Korean sentences to Chinese characters and then train the machine translation model with the converted Korean sentences as source sentences. The experimental results on Korean-to-Chinese translation demonstrate that the models with the proposed method improve translation quality up to 1.5 BLEU points in comparison to the baseline models.

Includes members:
  Member "semantic role labeling with associated memory network":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "document level neural machine translation with inter sentence attention")
  Member "dependency or span end to end uniform semantic role labeling":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "semantics aware bert for language understanding")
  Member "lattice based transformer encoder for neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "hierarchical contextualized representation for named entity recognition")
  Member "opennmt neural machine translation toolkit":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "evaluating sequence to sequence learning models for if then program synthesis")
  Member "finding better subword segmentation for neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "lattice based transformer encoder for neural machine translation")
  Member "exploring recombination for efficient decoding of neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "head driven phrase structure grammar parsing on penn treebank")
  Member "attention is all you need":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "attention focusing for neural machine translation by bridging source and target embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "controlling utterance length in nmt based word segmentation with attention")

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)