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: "factored neural machine translation"
  Abstract: We present a new approach for neural machine translation (NMT) using the morphological and grammatical decomposition of the words (factors) in the output side of the neural network. This architecture addresses two main problems occurring in MT, namely dealing with a large target language vocabulary and the out of vocabulary (OOV) words. By the means of factors, we are able to handle larger vocabulary and reduce the training time (for systems with equivalent target language vocabulary size). In addition, we can produce new words that are not in the vocabulary. We use a morphological analyser to get a factored representation of each word (lemmas, Part of Speech tag, tense, person, gender and number). We have extended the NMT approach with attention mechanism in order to have two different outputs, one for the lemmas and the other for the rest of the factors. The final translation is built using some \textit{a priori} linguistic information. We compare our extension with a word-based NMT system. The experiments, performed on the IWSLT'15 dataset translating from English to French, show that while the performance do not always increase, the system can manage a much larger vocabulary and consistently reduce the OOV rate. We observe up to 2% BLEU point improvement in a simulated out of domain translation setup.

Connected through hyperedge HE-1 (induced by source paper "using interlinear glosses as pivot in low resource multilingual machine translation"):
  with members:
    - "morphological word embeddings"
    - "character aware decoder for translation into morphologically rich languages"
    - "massively parallel cross lingual learning in low resource target language translation"
    - "improving character based decoding using target side morphological information for neural machine translation"
    - "opennmt open source toolkit for neural machine translation"
    - "six challenges for neural machine translation"
    - "toward multilingual neural machine translation with universal encoder and decoder"
    - "polyglot neural language models a case study in cross lingual phonetic representation learning"

Connected through hyperedge HE-2 (induced by source paper "machine translation of restaurant reviews new corpus for domain adaptation and robustness"):
  with members:
    - "tagged back translation"
    - "naver labs europe s systems for the wmt19 machine translation robustness task"
    - "improving robustness of machine translation with synthetic noise"
    - "findings of the first shared task on machine translation robustness"
    - "training on synthetic noise improves robustness to natural noise in machine translation"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "scaling neural machine translation"
    - "a call for clarity in reporting bleu scores"

Connected through hyperedge HE-3 (induced by source paper "low resource machine translation using interlinear glosses"):
  with members:
    - "revisiting low resource neural machine translation a case study"
    - "character aware decoder for translation into morphologically rich languages"
    - "adapting word embeddings to new languages with morphological and phonological subword representations"
    - "improving character based decoding using target side morphological information for neural machine translation"
    - "opennmt open source toolkit for neural machine translation"
    - "six challenges for neural machine translation"
    - "ensembling factored neural machine translation models for automatic post editing and quality estimation"
    - "toward multilingual neural machine translation with universal encoder and decoder"

Connected through hyperedge HE-4 (induced by source paper "diversity by phonetics and its application in neural machine translation"):
  with members:
    - "towards robust neural machine translation"
    - "attention is all you need"
    - "stronger baselines for trustable results in neural machine translation"
    - "convolutional sequence to sequence learning"
    - "linear algebraic structure of word senses with applications to polysemy"
    - "linguistic input features improve neural machine translation"
    - "multi source neural translation"
    - "a character level decoder without explicit segmentation for neural machine translation"

Connected through hyperedge HE-5 (induced by source paper "multi task deep morphological analyzer context aware joint morphological tagging and lemma prediction"):
  with members:
    - "neural machine translation for bilingually scarce scenarios a deep multi task learning approach"
    - "meta multi task learning for sequence modeling"
    - "scheduled multi task learning from syntax to translation"
    - "online and linear time attention by enforcing monotonic alignments"
    - "cross lingual character level neural morphological tagging"
    - "improving generalization performance by switching from adam to sgd"
    - "recent trends in deep learning based natural language processing"
    - "exploiting linguistic resources for neural machine translation using multi task learning"

Connected through hyperedge HE-6 (induced by source paper "logographic subword model for neural machine translation"):
  with members:
    - "convolutional sequence to sequence learning"
    - "nematus a toolkit for neural machine translation"
    - "deep architectures for neural machine translation"
    - "deep recurrent models with fast forward connections for neural machine translation"
    - "using the output embedding to improve language models"
    - "weight normalization a simple reparameterization to accelerate training of deep neural networks"
    - "theano a python framework for fast computation of mathematical expressions"
    - "linguistic input features improve neural machine translation"

Connected through hyperedge HE-7 (induced by source paper "improving character based decoding using target side morphological information for neural machine translation"):
  with members:
    - "doubly attentive decoder for multi modal neural machine translation"
    - "character based neural machine translation"
    - "achieving open vocabulary neural machine translation with hybrid word character models"
    - "linguistic input features improve neural machine translation"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "a character level decoder without explicit segmentation for neural machine translation"
    - "character aware neural language models"
    - "neural machine translation of rare words with subword units"

Connected through hyperedge HE-8 (induced by source paper "translating phrases in neural machine translation"):
  with members:
    - "neural system combination for machine translation"
    - "towards string to tree neural machine translation"
    - "modeling source syntax for neural machine translation"
    - "character based neural machine translation"
    - "pointing the unknown words"
    - "neural machine translation advised by statistical machine translation"
    - "tree to sequence attentional neural machine translation"
    - "neural machine translation with reconstruction"

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)