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: "guiding neural machine translation with retrieved translation pieces"
  Abstract: One of the difficulties of neural machine translation (NMT) is the recall and appropriate translation of low-frequency words or phrases. In this paper, we propose a simple, fast, and effective method for recalling previously seen translation examples and incorporating them into the NMT decoding process. Specifically, for an input sentence, we use a search engine to retrieve sentence pairs whose source sides are similar with the input sentence, and then collect $n$-grams that are both in the retrieved target sentences and aligned with words that match in the source sentences, which we call "translation pieces". We compute pseudo-probabilities for each retrieved sentence based on similarities between the input sentence and the retrieved source sentences, and use these to weight the retrieved translation pieces. Finally, an existing NMT model is used to translate the input sentence, with an additional bonus given to outputs that contain the collected translation pieces. We show our method improves NMT translation results up to 6 BLEU points on three narrow domain translation tasks where repetitiveness of the target sentences is particularly salient. It also causes little increase in the translation time, and compares favorably to another alternative retrieval-based method with respect to accuracy, speed, and simplicity of implementation.

Connected through hyperedge HE-1 (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"
    - "retrieval based neural code generation"
    - "the best of both worlds combining recent advances in neural machine translation"
    - "generating sentences by editing prototypes"

Connected through hyperedge HE-2 (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-3 (induced by source paper "instance based model adaptation for direct speech translation"):
  with members:
    - "attention passing models for robust and data efficient end to end speech translation"
    - "non parametric adaptation for neural machine translation"
    - "end to end speech translation with knowledge distillation"
    - "tied multitask learning for neural speech translation"
    - "end to end automatic speech translation of audiobooks"
    - "pre training on high resource speech recognition improves low resource speech to text translation"
    - "the ustc nel speech translation system at iwslt 2018"
    - "leveraging weakly supervised data to improve end to end speech to text translation"

Connected through hyperedge HE-4 (induced by source paper "automatic testing and improvement of machine translation"):
  with members:
    - "metamorphic testing a new approach for generating next test cases"
    - "machine learning testing survey landscapes and horizons"
    - "training on synthetic noise improves robustness to natural noise in machine translation"
    - "modeling recurrence for transformer"
    - "robust neural machine translation with doubly adversarial inputs"
    - "on the impact of various types of noise on neural machine translation"
    - "towards robust neural machine translation"
    - "achieving human parity on automatic chinese to english news translation"

Connected through hyperedge HE-5 (induced by source paper "retrieval based neural code generation"):
  with members:
    - "abstract syntax networks for code generation and semantic parsing"
    - "a syntactic neural model for general purpose code generation"
    - "language to logical form with neural attention"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "one sentence one model for neural machine translation"
    - "latent predictor networks for code generation"
    - "data recombination for neural semantic parsing"
    - "neural generation of regular expressions from natural language with minimal domain knowledge"

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)