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 "non autoregressive neural machine translation with enhanced decoder input"):
  Abstract: Non-autoregressive translation (NAT) models, which remove the dependence on previous target tokens from the inputs of the decoder, achieve significantly inference speedup but at the cost of inferior accuracy compared to autoregressive translation (AT) models. Previous work shows that the quality of the inputs of the decoder is important and largely impacts the model accuracy. In this paper, we propose two methods to enhance the decoder inputs so as to improve NAT models. The first one directly leverages a phrase table generated by conventional SMT approaches to translate source tokens to target tokens, which are then fed into the decoder as inputs. The second one transforms source-side word embeddings to target-side word embeddings through sentence-level alignment and word-level adversary learning, and then feeds the transformed word embeddings into the decoder as inputs. Experimental results show our method largely outperforms the NAT baseline~\citep{gu2017non} by $5.11$ BLEU scores on WMT14 English-German task and $4.72$ BLEU scores on WMT16 English-Romanian task.

Includes members:
  Member "sentence wise smooth regularization for sequence to sequence learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multilingual neural machine translation with knowledge distillation")
  Member "analyzing uncertainty in neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "discriminative adversarial search for abstractive summarization")
  Member "deterministic non autoregressive neural sequence modeling by iterative refinement":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "non autoregressive neural dialogue generation")
  Member "beyond error propagation in neural machine translation characteristics of language also matter":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "insertion based decoding with automatically inferred generation order")
  Member "fast decoding in sequence models using discrete latent variables":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "lava nat a non autoregressive translation model with look around decoding and vocabulary attention")
  Member "double path networks for sequence to sequence learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "fairseq a fast extensible toolkit for sequence modeling")
  Member "dense information flow for neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "mass masked sequence to sequence pre training for language generation")
  Member "attention is all you need":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")

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)