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: "deep recurrent models with fast forward connections for neural machine translation"
  Abstract: Neural machine translation (NMT) aims at solving machine translation (MT) problems using neural networks and has exhibited promising results in recent years. However, most of the existing NMT models are shallow and there is still a performance gap between a single NMT model and the best conventional MT system. In this work, we introduce a new type of linear connections, named fast-forward connections, based on deep Long Short-Term Memory (LSTM) networks, and an interleaved bi-directional architecture for stacking the LSTM layers. Fast-forward connections play an essential role in propagating the gradients and building a deep topology of depth 16. On the WMT'14 English-to-French task, we achieve BLEU=37.7 with a single attention model, which outperforms the corresponding single shallow model by 6.2 BLEU points. This is the first time that a single NMT model achieves state-of-the-art performance and outperforms the best conventional model by 0.7 BLEU points. We can still achieve BLEU=36.3 even without using an attention mechanism. After special handling of unknown words and model ensembling, we obtain the best score reported to date on this task with BLEU=40.4. Our models are also validated on the more difficult WMT'14 English-to-German task.

Connected through hyperedge HE-1 (induced by source paper "multi layer representation fusion for neural machine translation"):
  with members:
    - "path aggregation network for instance segmentation"
    - "towards neural phrase based machine translation"
    - "attention is all you need"
    - "stronger baselines for trustable results in neural machine translation"
    - "classical structured prediction losses for sequence to sequence learning"
    - "multi channel encoder for neural machine translation"
    - "convolutional sequence to sequence learning"
    - "deep neural machine translation with linear associative unit"

Connected through hyperedge HE-2 (induced by source paper "fundamentals of recurrent neural network rnn and long short term memory lstm network"):
  with members:
    - "latent odes for irregularly sampled time series"
    - "long short term memory as a dynamically computed element wise weighted sum"
    - "stable architectures for deep neural networks"
    - "deep neural networks motivated by partial differential equations"
    - "neural ordinary differential equations"
    - "recent advances in recurrent neural networks"
    - "nais net stable deep networks from non autonomous differential equations"
    - "lstm a search space odyssey"

Connected through hyperedge HE-3 (induced by source paper "bi decoder augmented network for neural machine translation"):
  with members:
    - "reinforced dynamic reasoning for conversational question generation"
    - "discourse marker augmented network with reinforcement learning for natural language inference"
    - "macnet transferring knowledge from machine comprehension to sequence to sequence models"
    - "soft layer specific multi task summarization with entailment and question generation"
    - "attention is all you need"
    - "unsupervised neural machine translation"
    - "offline bilingual word vectors orthogonal transformations and the inverted softmax"
    - "what do neural machine translation models learn about morphology"

Connected through hyperedge HE-4 (induced by source paper "marathi to english neural machine translation with near perfect corpus and transformers"):
  with members:
    - "massively multilingual neural machine translation in the wild findings and challenges"
    - "achieving human parity on automatic chinese to english news translation"
    - "training deeper neural machine translation models with transparent attention"
    - "the best of both worlds combining recent advances in neural machine translation"
    - "sockeye a toolkit for neural machine translation"
    - "attention is all you need"
    - "convolutional sequence to sequence learning"
    - "toward multilingual neural machine translation with universal encoder and decoder"

Connected through hyperedge HE-5 (induced by source paper "on the linguistic representational power of neural machine translation models"):
  with members:
    - "to tune or not to tune adapting pretrained representations to diverse tasks"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "the emergence of number and syntax units in lstm language models"
    - "a hybrid morpheme word representation for machine translation of morphologically rich languages"
    - "linguistic knowledge and transferability of contextual representations"
    - "the missing ingredient in zero shot neural machine translation"
    - "language modeling teaches you more syntax than translation does lessons learned through auxiliary task analysis"
    - "bpe and charcnns for translation of morphology a cross lingual comparison and analysis"

Connected through hyperedge HE-6 (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-7 (induced by source paper "dynamic layer aggregation for neural machine translation with routing by agreement"):
  with members:
    - "multi layer representation fusion for neural machine translation"
    - "context aware self attention networks"
    - "neural machine translation with adequacy oriented learning"
    - "achieving human parity on automatic chinese to english news translation"
    - "exploiting deep representations for neural machine translation"
    - "tied multitask learning for neural speech translation"
    - "modeling localness for self attention networks"
    - "deep contextualized word representations"

Connected through hyperedge HE-8 (induced by source paper "synchronous bidirectional inference for neural sequence generation"):
  with members:
    - "synchronous bidirectional neural machine translation"
    - "pay less attention with lightweight and dynamic convolutions"
    - "bridging the gap between training and inference for neural machine translation"
    - "asynchronous bidirectional decoding for neural machine translation"
    - "regularizing neural machine translation by target bidirectional agreement"
    - "self attention with relative position representations"
    - "pervasive attention 2d convolutional neural networks for sequence to sequence prediction"
    - "the best of both worlds combining recent advances in 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)