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: "domain robustness in neural machine translation"
  Abstract: Translating text that diverges from the training domain is a key challenge for neural machine translation (NMT). Domain robustness - the generalization of models to unseen test domains - is low compared to statistical machine translation. In this paper, we investigate the performance of NMT on out-of-domain test sets, and ways to improve it. #R##N#We observe that hallucination (translations that are fluent but unrelated to the source) is common in out-of-domain settings, and we empirically compare methods that improve adequacy (reconstruction), out-of-domain translation (subword regularization), or robustness against adversarial examples (defensive distillation), as well as noisy channel models. #R##N#In experiments on German to English OPUS data, and German to Romansh, a low-resource scenario, we find that several methods improve domain robustness, reconstruction standing out as a method that not only improves automatic scores, but also shows improvements in a manual assessments of adequacy, albeit at some loss in fluency. However, out-of-domain performance is still relatively low and domain robustness remains an open problem.

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)