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 "six challenges for neural machine translation"):
  Abstract: We explore six challenges for neural machine translation: domain mismatch, amount of training data, rare words, long sentences, word alignment, and beam search. We show both deficiencies and improvements over the quality of phrase-based statistical machine translation.

Includes members:
  Member "is neural machine translation ready for deployment a case study on 30 translation directions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "cued wmt19 ewc lms")
  Member "neural machine translation with supervised attention":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "an empirical study of spatial attention mechanisms in deep networks")
  Member "guided alignment training for topic aware neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "large scale pretraining for neural machine translation with tens of billions of sentence pairs")
  Member "fast domain adaptation for neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "unsupervised domain adaptation for neural machine translation with iterative back translation")
  Member "incorporating discrete translation lexicons into neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "latent relation language models")
  Member "neural machine translation of rare words with subword units":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "neural machine translation by jointly learning to align and translate":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")

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)