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 "achieving open vocabulary neural machine translation with hybrid word character models"):
  Abstract: Nearly all previous work on neural machine translation (NMT) has used quite restricted vocabularies, perhaps with a subsequent method to patch in unknown words. This paper presents a novel word-character solution to achieving open vocabulary NMT. We build hybrid systems that translate mostly at the word level and consult the character components for rare words. Our character-level recurrent neural networks compute source word representations and recover unknown target words when needed. The twofold advantage of such a hybrid approach is that it is much faster and easier to train than character-based ones; at the same time, it never produces unknown words as in the case of word-based models. On the WMT'15 English to Czech translation task, this hybrid approach offers an addition boost of +2.1-11.4 BLEU points over models that already handle unknown words. Our best system achieves a new state-of-the-art result with 20.7 BLEU score. We demonstrate that our character models can successfully learn to not only generate well-formed words for Czech, a highly-inflected language with a very complex vocabulary, but also build correct representations for English source words.

Includes members:
  Member "exploring the limits of language modeling":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a survey on contextual embeddings")
  Member "character based neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "using interlinear glosses as pivot in low resource multilingual machine translation")
  Member "a hierarchical neural autoencoder for paragraphs and documents":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "denoising based sequence to sequence pre training for text generation")
  Member "listen attend and spell":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "speech corpus of ainu folklore and end to end speech recognition for ainu language")
  Member "effective approaches to attention based neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "attention over parameters for dialogue systems")
  Member "improved transition based parsing by modeling characters instead of words with lstms":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "transition based dependency parsing using perceptron learner")
  Member "end to end attention based large vocabulary speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "refined gate a simple and effective gating mechanism for recurrent units")
  Member "character aware neural language models":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "trends and advancements in deep neural network communication")

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)