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: "unsupervised neural machine translation initialized by unsupervised statistical machine translation"
  Abstract: Recent work achieved remarkable results in training neural machine translation (NMT) systems in a fully unsupervised way, with new and dedicated architectures that rely on monolingual corpora only. In this work, we propose to define unsupervised NMT (UNMT) as NMT trained with the supervision of synthetic bilingual data. Our approach straightforwardly enables the use of state-of-the-art architectures proposed for supervised NMT by replacing human-made bilingual data with synthetic bilingual data for training. We propose to initialize the training of UNMT with synthetic bilingual data generated by unsupervised statistical machine translation (USMT). The UNMT system is then incrementally improved using back-translation. Our preliminary experiments show that our approach achieves a new state-of-the-art for unsupervised machine translation on the WMT16 German--English news translation task, for both translation directions.

Connected through hyperedge HE-1 (induced by source paper "do all roads lead to rome understanding the role of initialization in iterative back translation"):
  with members:
    - "multilingual denoising pre training for neural machine translation"
    - "using monolingual data in neural machine translation a systematic study"
    - "an effective approach to unsupervised machine translation"
    - "mass masked sequence to sequence pre training for language generation"
    - "investigating backtranslation in neural machine translation"
    - "unsupervised statistical machine translation"
    - "scaling neural machine translation"
    - "phrase based neural unsupervised machine translation"

Connected through hyperedge HE-2 (induced by source paper "explicit cross lingual pre training for unsupervised machine translation"):
  with members:
    - "mass masked sequence to sequence pre training for language generation"
    - "unsupervised neural machine translation with smt as posterior regularization"
    - "cross lingual language model pretraining"
    - "unsupervised statistical machine translation"
    - "xnli evaluating cross lingual sentence representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "joint training for neural machine translation models with monolingual data"
    - "a robust self learning method for fully unsupervised cross lingual mappings of word embeddings"

Connected through hyperedge HE-3 (induced by source paper "an effective approach to unsupervised machine translation"):
  with members:
    - "unsupervised neural machine translation with smt as posterior regularization"
    - "cross lingual language model pretraining"
    - "achieving human parity on automatic chinese to english news translation"
    - "unsupervised statistical machine translation"
    - "scaling neural machine translation"
    - "a call for clarity in reporting bleu scores"
    - "a robust self learning method for fully unsupervised cross lingual mappings of word embeddings"
    - "phrase based neural unsupervised machine translation"

Connected through hyperedge HE-4 (induced by source paper "towards unsupervised grammatical error correction using statistical machine translation with synthetic comparable corpus"):
  with members:
    - "improving grammatical error correction via pre training a copy augmented architecture with unlabeled data"
    - "unsupervised statistical machine translation"
    - "near human level performance in grammatical error correction with hybrid machine translation"
    - "reaching human level performance in automatic grammatical error correction an empirical study"
    - "a robust self learning method for fully unsupervised cross lingual mappings of word embeddings"
    - "phrase based neural unsupervised machine translation"
    - "cgmh constrained sentence generation by metropolis hastings sampling"
    - "jfleg a fluency corpus and benchmark for grammatical error correction"

Connected through hyperedge HE-5 (induced by source paper "exploiting out of domain parallel data through multilingual transfer learning for low resource neural machine translation"):
  with members:
    - "unsupervised statistical machine translation"
    - "trivial transfer learning for low resource neural machine translation"
    - "a comparison of transformer and recurrent neural networks on multilingual neural machine translation"
    - "improving zero shot translation of low resource languages"
    - "phrase based neural unsupervised machine translation"
    - "bi directional neural machine translation with synthetic parallel data"
    - "attention is all you need"
    - "six challenges for 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)