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: "search engine guided non parametric neural machine translation"
  Abstract: In this paper, we extend an attention-based neural machine translation (NMT) model by allowing it to access an entire training set of parallel sentence pairs even after training. The proposed approach consists of two stages. In the first stage--retrieval stage--, an off-the-shelf, black-box search engine is used to retrieve a small subset of sentence pairs from a training set given a source sentence. These pairs are further filtered based on a fuzzy matching score based on edit distance. In the second stage--translation stage--, a novel translation model, called translation memory enhanced NMT (TM-NMT), seamlessly uses both the source sentence and a set of retrieved sentence pairs to perform the translation. Empirical evaluation on three language pairs (En-Fr, En-De, and En-Es) shows that the proposed approach significantly outperforms the baseline approach and the improvement is more significant when more relevant sentence pairs were retrieved.

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"
    - "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-2 (induced by source paper "coupling retrieval and meta learning for context dependent semantic parsing"):
  with members:
    - "learning to map context dependent sentences to executable formal queries"
    - "dictionary guided editing networks for paraphrase generation"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "response generation by context aware prototype editing"
    - "mapping language to code in programmatic context"
    - "situated mapping of sequential instructions to actions with single step reward observation"
    - "decoupling structure and lexicon for zero shot semantic parsing"
    - "spherical latent spaces for stable variational autoencoders"

Connected through hyperedge HE-3 (induced by source paper "improving multi turn dialogue modelling with utterance rewriter"):
  with members:
    - "review driven answer generation for product related questions in e commerce"
    - "sentence rewriting for semantic parsing"
    - "cota improving the speed and accuracy of customer support through ranking and deep networks"
    - "retrieve and refine improved sequence generation models for dialogue"
    - "fast abstractive summarization with reinforce selected sentence rewriting"
    - "an exploration of neural sequence to sequence architectures for automatic post editing"
    - "end to end neural coreference resolution"
    - "quickedit editing text translations by crossing words out"

Connected through hyperedge HE-4 (induced by source paper "training neural machine translation to apply terminology constraints"):
  with members:
    - "towards one shot learning for rare word translation with external experts"
    - "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"
    - "effective approaches to attention based neural machine translation"
    - "neural machine translation of rare words with subword units"

Connected through hyperedge HE-5 (induced by source paper "retrieval augmented convolutional neural networks for improved robustness against adversarial examples"):
  with members:
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "adversarial spheres"
    - "generating sentences by editing prototypes"
    - "no need to worry about adversarial examples in object detection in autonomous vehicles"
    - "billion scale similarity search with gpus"
    - "generative adversarial trainer defense to adversarial perturbations with gan"
    - "foolbox v0 8 0 a python toolbox to benchmark the robustness of machine learning models"
    - "mixup beyond empirical risk minimization"

Connected through hyperedge HE-6 (induced by source paper "fast parametric learning with activation memorization"):
  with members:
    - "deep meta learning learning to learn in the concept space"
    - "breaking the softmax bottleneck a high rank rnn language model"
    - "on the state of the art of evaluation in neural language models"
    - "learning to remember rare events"
    - "learning to create and reuse words in open vocabulary neural language modeling"
    - "unbounded cache model for online language modeling with open vocabulary"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "pointing the unknown words"

Connected through hyperedge HE-7 (induced by source paper "translating pro drop languages with reconstruction models"):
  with members:
    - "translating phrases in neural machine translation"
    - "learning to remember translation history with a continuous cache"
    - "chunk based bi scale decoder for neural machine translation"
    - "exploiting cross sentence context for neural machine translation"
    - "modeling source syntax for neural machine translation"
    - "neural machine translation advised by statistical machine translation"
    - "context gates for neural machine translation"
    - "neural machine translation with reconstruction"

Connected through hyperedge HE-8 (induced by source paper "retrieve and refine improved sequence generation models for dialogue"):
  with members:
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "generating sentences by editing prototypes"
    - "an exploration of neural sequence to sequence architectures for automatic post editing"
    - "quickedit editing text translations by crossing words out"
    - "adapting sequence models for sentence correction"
    - "controllable abstractive summarization"
    - "iterative refinement for machine translation"
    - "how not to evaluate your dialogue system an empirical study of unsupervised evaluation metrics for dialogue response generation"

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)