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 "global context neural machine translation through target side attentive residual connections"):
  Abstract: Neural sequence-to-sequence models achieve remarkable performance not only in machine translation (MT) but also in other language processing tasks. One of the reasons for their effectiveness is the ability of their decoder to capture contextual information through its recurrent layer. However, its sequential modeling over-emphasizes the local context, i.e. the previously translated word in the case of MT. As a result, the model ignores important information from the global context of the translation. In this paper, we address this limitation by introducing attentive residual connections from the previously translated words to the output of the decoder, which enable the learning of longer-range dependencies between words. The proposed model can emphasize any of the previously translated words, as opposed to only the last one, gaining access to the global context of the translated text. The model outperforms strong neural MT baselines on three language pairs, as well as a neural language modeling baseline. The analysis of the attention learned by the decoder confirms that it emphasizes a wide context, and reveals resemblance to syntactic-like structures.

Includes members:
  Member "recurrent additive networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a lightweight recurrent network for sequence modeling")
  Member "predicting target language ccg supertags improves neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on the linguistic representational power of neural machine translation models")
  Member "learning structured text representations":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "structsum incorporating latent and explicit sentence dependencies for single document summarization")
  Member "residual lstm design of a deep recurrent architecture for distant speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "refined gate a simple and effective gating mechanism for recurrent units")
  Member "attention is all you need":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "frustratingly short attention spans in neural language modeling":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "state regularized recurrent neural networks")
  Member "towards string to tree neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "structural language models of code")
  Member "rra recurrent residual attention for sequence learning":
    also connected through other hyperedges:
      (none)

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)