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: "effective cross lingual transfer of neural machine translation models without shared vocabularies"
  Abstract: Transfer learning or multilingual model is essential for low-resource neural machine translation (NMT), but the applicability is limited to cognate languages by sharing their vocabularies. This paper shows effective techniques to transfer a pre-trained NMT model to a new, unrelated language without shared vocabularies. We relieve the vocabulary mismatch by using cross-lingual word embedding, train a more language-agnostic encoder by injecting artificial noises, and generate synthetic data easily from the pre-training data without back-translation. Our methods do not require restructuring the vocabulary or retraining the model. We improve plain NMT transfer by up to +5.1% BLEU in five low-resource translation tasks, outperforming multilingual joint training by a large margin. We also provide extensive ablation studies on pre-trained embedding, synthetic data, vocabulary size, and parameter freezing for a better understanding of NMT transfer.

Connected through hyperedge HE-1 (induced by source paper "exploring benefits of transfer learning in neural machine translation"):
  with members:
    - "widening the representation bottleneck in neural machine translation with lexical shortcuts"
    - "revisiting low resource neural machine translation a case study"
    - "tackling climate change with machine learning"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "energy and policy considerations for deep learning in nlp"
    - "investigating backtranslation in neural machine translation"
    - "a survey of domain adaptation for neural machine translation"
    - "characterizing and avoiding negative transfer"

Connected through hyperedge HE-2 (induced by source paper "cross lingual pre training based transfer for zero shot neural machine translation"):
  with members:
    - "pivot based transfer learning for neural machine translation between non english languages"
    - "improved zero shot neural machine translation via ignoring spurious correlations"
    - "unicoder a universal language encoder by pre training with multiple cross lingual tasks"
    - "consistency by agreement in zero shot neural machine translation"
    - "unsupervised neural machine translation with smt as posterior regularization"
    - "how multilingual is multilingual bert"
    - "cross lingual language model pretraining"
    - "massively multilingual neural machine translation"

Connected through hyperedge HE-3 (induced by source paper "a universal parent model for low resource neural machine translation transfer"):
  with members:
    - "trivial transfer learning for low resource neural machine translation"
    - "universal neural machine translation for extremely low resource languages"
    - "when and why are pre trained word embeddings useful for neural machine translation"
    - "rapid adaptation of neural machine translation to new languages"
    - "transfer learning in multilingual neural machine translation with dynamic vocabulary"
    - "attention is all you need"
    - "convolutional sequence to sequence learning"
    - "transfer learning across low resource related languages for neural machine translation"

Connected through hyperedge HE-4 (induced by source paper "pivot based transfer learning for neural machine translation between non english languages"):
  with members:
    - "massively multilingual neural machine translation"
    - "filtering and mining parallel data in a joint multilingual space"
    - "trivial transfer learning for low resource neural machine translation"
    - "triangular architecture for rare language 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-5 (induced by source paper "improving zero shot translation with language independent constraints"):
  with members:
    - "consistency by agreement in zero shot neural machine translation"
    - "the missing ingredient in zero shot neural machine translation"
    - "universal neural machine translation for extremely low resource languages"
    - "a neural interlingua for multilingual machine translation"
    - "contextual parameter generation for universal neural machine translation"
    - "a teacher student framework for zero resource neural machine translation"
    - "learning joint multilingual sentence representations with neural machine translation"
    - "attention is all you need"

Connected through hyperedge HE-6 (induced by source paper "from bilingual to multilingual neural machine translation by incremental training"):
  with members:
    - "multilingual multi scale and multi layer visualization of intermediate representations"
    - "a neural interlingua for multilingual machine translation"
    - "transfer learning in multilingual neural machine translation with dynamic vocabulary"
    - "learning joint multilingual sentence representations with neural machine translation"
    - "attention is all you need"
    - "unsupervised neural machine translation"
    - "zero resource translation with multi lingual neural machine translation"
    - "multi way multilingual neural machine translation with a shared attention mechanism"

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)