human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Question: Please tell me which class the center node 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)