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: "neural system combination for machine translation"
  Abstract: Neural machine translation (NMT) becomes a new approach to machine translation and generates much more fluent results compared to statistical machine translation (SMT). #R##N#However, SMT is usually better than NMT in translation adequacy. It is therefore a promising direction to combine the advantages of both NMT and SMT. #R##N#In this paper, we propose a neural system combination framework leveraging multi-source NMT, which takes as input the outputs of NMT and SMT systems and produces the final translation. #R##N#Extensive experiments on the Chinese-to-English translation task show that our model archives significant improvement by 5.3 BLEU points over the best single system output and 3.4 BLEU points over the state-of-the-art traditional system combination methods.

Connected through hyperedge HE-1 (induced by source paper "training simplification and model simplification for deep learning a minimal effort back propagation method"):
  with members:
    - "bag of words as target for neural machine translation"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "attention is all you need"
    - "nonparametric neural networks"
    - "sparse communication for distributed gradient descent"
    - "meprop sparsified back propagation for accelerated deep learning with reduced overfitting"
    - "exploring sparsity in recurrent neural networks"
    - "deep neural machine translation with linear associative unit"

Connected through hyperedge HE-2 (induced by source paper "cued wmt19 ewc lms"):
  with members:
    - "selective attention for context aware neural machine translation"
    - "domain adaptive inference for neural machine translation"
    - "investigating backtranslation in neural machine translation"
    - "modeling coherence for discourse neural machine translation"
    - "has machine translation achieved human parity a case for document level evaluation"
    - "near human level performance in grammatical error correction with hybrid machine translation"
    - "improving the transformer translation model with document level context"
    - "simple fusion return of the language model"

Connected through hyperedge HE-3 (induced by source paper "non parametric adaptation for neural machine translation"):
  with members:
    - "lingvo a modular and scalable framework for sequence to sequence modeling"
    - "retrieve and refine improved sequence generation models for dialogue"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "effective parallel corpus mining using bilingual sentence embeddings"
    - "improving the transformer translation model with document level context"
    - "guiding neural machine translation with retrieved translation pieces"
    - "retrieval based neural code generation"
    - "the best of both worlds combining recent advances in neural machine translation"

Connected through hyperedge HE-4 (induced by source paper "robust machine translation with domain sensitive pseudo sources baidu osu wmt19 mt robustness shared task system report"):
  with members:
    - "simultaneous translation with flexible policy via restricted imitation learning"
    - "improving robustness of machine translation with synthetic noise"
    - "findings of the first shared task on machine translation robustness"
    - "end to end speech translation with knowledge distillation"
    - "a survey of domain adaptation for neural machine translation"
    - "switchout an efficient data augmentation algorithm for neural machine translation"
    - "robust neural machine translation with joint textual and phonetic embedding"
    - "multi reference training with pseudo references for neural translation and text generation"

Connected through hyperedge HE-5 (induced by source paper "synchronous bidirectional neural machine translation"):
  with members:
    - "asynchronous bidirectional decoding for neural machine translation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "breaking the beam search curse a study of re scoring methods and stopping criteria for neural machine translation"
    - "the university of edinburgh s neural mt systems for wmt17"
    - "attention is all you need"
    - "modeling past and future for neural machine translation"
    - "learning to decode for future success"
    - "convolutional sequence to sequence learning"

Connected through hyperedge HE-6 (induced by source paper "learning to reuse translations guiding neural machine translation with examples"):
  with members:
    - "exploiting deep representations for neural machine translation"
    - "skeleton to response dialogue generation guided by retrieval memory"
    - "tied multitask learning for neural speech translation"
    - "learning to jointly translate and predict dropped pronouns with a shared reconstruction mechanism"
    - "improving the transformer translation model with document level context"
    - "parameter sharing methods for multilingual self attentional translation models"
    - "fusing recency into neural machine translation with an inter sentence gate model"
    - "deep contextualized word representations"

Connected through hyperedge HE-7 (induced by source paper "flexner a flexible lstm cnn stack framework for named entity recognition"):
  with members:
    - "gsn a graph structured network for multi party dialogues"
    - "design challenges and misconceptions in neural sequence labeling"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "semi supervised sequence modeling with cross view training"
    - "a discourse level named entity recognition and relation extraction dataset for chinese literature text"
    - "transfer learning for sequence tagging with hierarchical recurrent networks"
    - "improving multilingual named entity recognition with wikipedia entity type mapping"

Connected through hyperedge HE-8 (induced by source paper "think again networks and the delta loss"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "asynchronous bidirectional decoding for neural machine translation"
    - "direct output connection for a high rank language model"
    - "trellis networks for sequence modeling"
    - "an exploration of neural sequence to sequence architectures for automatic post editing"
    - "breaking the softmax bottleneck a high rank rnn language model"
    - "attention is all you need"
    - "regularizing and optimizing lstm language models"

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)