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 machine translation leveraging phrase based models in a hybrid search"
  Abstract: In this paper, we introduce a hybrid search for attention-based neural machine translation (NMT). A target phrase learned with statistical MT models extends a hypothesis in the NMT beam search when the attention of the NMT model focuses on the source words translated by this phrase. Phrases added in this way are scored with the NMT model, but also with SMT features including phrase-level translation probabilities and a target language model. Experimental results on German->English news domain and English->Russian e-commerce domain translation tasks show that using phrase-based models in NMT search improves MT quality by up to 2.3% BLEU absolute as compared to a strong NMT baseline.

Connected through hyperedge HE-1 (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-2 (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-3 (induced by source paper "merging external bilingual pairs into neural machine translation"):
  with members:
    - "code switching for enhancing nmt with pre specified translation"
    - "scaling neural machine translation"
    - "prior knowledge integration for neural machine translation using posterior regularization"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "phrase based neural unsupervised machine translation"
    - "translating phrases in neural machine translation"
    - "lexically constrained decoding for sequence generation using grid beam search"
    - "unsupervised neural machine translation"

Connected through hyperedge HE-4 (induced by source paper "neural machine translation with explicit phrase alignment"):
  with members:
    - "fast lexically constrained decoding with dynamic beam allocation for neural machine translation"
    - "neural machine translation decoding with terminology constraints"
    - "attention is all you need"
    - "lexically constrained decoding for sequence generation using grid beam search"
    - "six challenges for neural machine translation"
    - "syntactically guided neural machine translation"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "neural machine translation of rare words with subword units"

Connected through hyperedge HE-5 (induced by source paper "the university of cambridge s machine translation systems for wmt18"):
  with members:
    - "achieving human parity on automatic chinese to english news translation"
    - "weighted transformer network for machine translation"
    - "near human level performance in grammatical error correction with hybrid machine translation"
    - "self attention with relative position representations"
    - "training tips for the transformer model"
    - "why not be versatile applications of the sgnmt decoder for machine translation"
    - "fine grained evaluation of german english machine translation based on a test suite"
    - "accelerating nmt batched beam decoding with lmbr posteriors for deployment"

Connected through hyperedge HE-6 (induced by source paper "sequential copying networks"):
  with members:
    - "translating phrases in neural machine translation"
    - "selective encoding for abstractive sentence summarization"
    - "neural question generation from text a preliminary study"
    - "pointing the unknown words"
    - "abstractive text summarization using sequence to sequence rnns and beyond"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "squad 100 000 questions for machine comprehension of text"
    - "machine comprehension using match lstm and answer pointer"

Connected through hyperedge HE-7 (induced by source paper "phrase based attentions"):
  with members:
    - "translating phrases in neural machine translation"
    - "attention is all you need"
    - "depthwise separable convolutions for neural machine translation"
    - "neural machine translation in linear time"
    - "neural machine translation with external phrase memory"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "effective approaches to attention based neural machine translation"
    - "neural machine translation of rare words with subword units"

Connected through hyperedge HE-8 (induced by source paper "neural and statistical methods for leveraging meta information in machine translation"):
  with members:
    - "guided alignment training for topic aware neural machine translation"
    - "target side context for discriminative models in statistical machine translation"
    - "improving neural machine translation models with monolingual data"
    - "neural machine translation by jointly learning to align and translate"
    - "on using very large target vocabulary 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)