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: "triangular architecture for rare language translation"
  Abstract: Neural Machine Translation (NMT) performs poor on the low-resource language pair $(X,Z)$, especially when $Z$ is a rare language. By introducing another rich language $Y$, we propose a novel triangular training architecture (TA-NMT) to leverage bilingual data $(Y,Z)$ (may be small) and $(X,Y)$ (can be rich) to improve the translation performance of low-resource pairs. In this triangular architecture, $Z$ is taken as the intermediate latent variable, and translation models of $Z$ are jointly optimized with a unified bidirectional EM algorithm under the goal of maximizing the translation likelihood of $(X,Y)$. Empirical results demonstrate that our method significantly improves the translation quality of rare languages on MultiUN and IWSLT2012 datasets, and achieves even better performance combining back-translation methods.

Connected through hyperedge HE-1 (induced by source paper "improving back translation with uncertainty based confidence estimation"):
  with members:
    - "openkiwi an open source framework for quality estimation"
    - "investigating backtranslation in neural machine translation"
    - "quantifying uncertainties in natural language processing tasks"
    - "achieving human parity on automatic chinese to english news translation"
    - "confidence modeling for neural semantic parsing"
    - "phrase based neural unsupervised machine translation"
    - "modeling uncertainty with hedged instance embedding"
    - "understanding back translation at scale"

Connected through hyperedge HE-2 (induced by source paper "pivot based transfer learning for neural machine translation between non english languages"):
  with members:
    - "effective cross lingual transfer of neural machine translation models without shared vocabularies"
    - "massively multilingual neural machine translation"
    - "filtering and mining parallel data in a joint multilingual space"
    - "trivial transfer learning for low resource neural machine translation"
    - "improving zero shot translation of low resource languages"
    - "a neural interlingua for multilingual machine translation"
    - "zero shot dual machine translation"
    - "understanding back translation at scale"

Connected through hyperedge HE-3 (induced by source paper "the missing ingredient in zero shot neural machine translation"):
  with members:
    - "lingvo a modular and scalable framework for sequence to sequence modeling"
    - "universal neural machine translation for extremely low resource languages"
    - "a neural interlingua for multilingual machine translation"
    - "contextual parameter generation for universal neural machine translation"
    - "phrase based neural unsupervised machine translation"
    - "zero shot dual machine translation"
    - "unsupervised neural machine translation with weight sharing"
    - "the best of both worlds combining recent advances in neural machine translation"

Connected through hyperedge HE-4 (induced by source paper "enhancing the robustness of prior network in out of distribution detection"):
  with members:
    - "achieving human parity on automatic chinese to english news translation"
    - "evidential deep learning to quantify classification uncertainty"
    - "enhancing the reliability of out of distribution image detection in neural networks"
    - "attention is all you need"
    - "generative bridging network in neural sequence prediction"
    - "regularizing neural networks by penalizing confident output distributions"
    - "aggregated residual transformations for deep neural networks"
    - "guided alignment training for topic aware neural machine translation"

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)