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 "neural machine translation with supervised attention"):
  Abstract: The attention mechanisim is appealing for neural machine translation, since it is able to dynam- ically encode a source sentence by generating a alignment between a target word and source words. Unfortunately, it has been proved to be worse than conventional alignment models in aligment accuracy. In this paper, we analyze and explain this issue from the point view of re- ordering, and propose a supervised attention which is learned with guidance from conventional alignment models. Experiments on two Chinese-to-English translation tasks show that the super- vised attention mechanism yields better alignments leading to substantial gains over the standard attention based NMT.

Includes members:
  Member "a survey of word reordering in statistical machine translation computational models and language phenomena":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "handling syntactic divergence in low resource machine translation")
  Member "implicit distortion and fertility models for attention based encoder decoder nmt model":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "controlling utterance length in nmt based word segmentation with attention")
  Member "attention correctness in neural image captioning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "counterfactual samples synthesizing for robust visual question answering")
  Member "guided alignment training for topic aware neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "large scale pretraining for neural machine translation with tens of billions of sentence pairs")
  Member "modeling coverage for neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "training simplification and model simplification for deep learning a minimal effort back propagation method")
  Member "supervised attentions for neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "relevance promoting language model for short text conversation")
  Member "incorporating structural alignment biases into an attentional neural translation model":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "an empirical study of spatial attention mechanisms in deep networks")
  Member "incorporating discrete translation lexicons into neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "latent relation language models")

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)