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: "improving character based decoding using target side morphological information for neural machine translation"
  Abstract: Recently, neural machine translation (NMT) has emerged as a powerful alternative to conventional statistical approaches. However, its performance drops considerably in the presence of morphologically rich languages (MRLs). Neural engines usually fail to tackle the large vocabulary and high out-of-vocabulary (OOV) word rate of MRLs. Therefore, it is not suitable to exploit existing word-based models to translate this set of languages. In this paper, we propose an extension to the state-of-the-art model of Chung et al. (2016), which works at the character level and boosts the decoder with target-side morphological information. In our architecture, an additional morphology table is plugged into the model. Each time the decoder samples from a target vocabulary, the table sends auxiliary signals from the most relevant affixes in order to enrich the decoder's current state and constrain it to provide better predictions. We evaluated our model to translate English into German, Russian, and Turkish as three MRLs and observed significant improvements.

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"
    - "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"
    - "multi way multilingual neural machine translation with a shared attention mechanism"

Connected through hyperedge HE-2 (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"
    - "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"
    - "polyglot neural language models a case study in cross lingual phonetic representation learning"

Connected through hyperedge HE-3 (induced by source paper "multitask learning for different subword segmentations in neural machine translation"):
  with members:
    - "revisiting character based neural machine translation with capacity and compression"
    - "optimizing segmentation granularity for neural machine translation"
    - "learning to segment inputs for nmt favors character level processing"
    - "subword regularization improving neural network translation models with multiple subword candidates"
    - "a call for clarity in reporting bleu scores"
    - "nmtpy a flexible toolkit for advanced neural machine translation systems"
    - "attention is all you need"
    - "exploiting linguistic resources for neural machine translation using multi task learning"

Connected through hyperedge HE-4 (induced by source paper "character aware decoder for translation into morphologically rich languages"):
  with members:
    - "revisiting character based neural machine translation with capacity and compression"
    - "bpe and charcnns for translation of morphology a cross lingual comparison and analysis"
    - "convolutional sequence to sequence learning"
    - "document context neural machine translation with memory networks"
    - "character based neural machine translation"
    - "gated word character recurrent language model"
    - "a character level decoder without explicit segmentation for neural machine translation"
    - "effective approaches to attention based neural machine translation"

Connected through hyperedge HE-5 (induced by source paper "optimizing segmentation granularity for neural machine translation"):
  with members:
    - "growing a brain fine tuning by increasing model capacity"
    - "subword regularization improving neural network translation models with multiple subword candidates"
    - "lifelong learning with dynamically expandable networks"
    - "modeling target side inflection in neural machine translation"
    - "linguistically motivated vocabulary reduction for neural machine translation from turkish to english"
    - "achieving open vocabulary neural machine translation with hybrid word character models"
    - "a character level decoder without explicit segmentation for neural machine translation"
    - "finding function in form compositional character models for open vocabulary word representation"

Connected through hyperedge HE-6 (induced by source paper "lost in translation analysis of information loss during machine translation between polysynthetic and fusional languages"):
  with members:
    - "compositional representation of morphologically rich input for neural machine translation"
    - "are all languages equally hard to language model"
    - "from characters to words to in between do we capture morphology"
    - "what do neural machine translation models learn about morphology"
    - "fully character level neural machine translation without explicit segmentation"
    - "neural machine translation of rare words with subword units"

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)