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: "linguistically motivated vocabulary reduction for neural machine translation from turkish to english"
  Abstract: The necessity of using a fixed-size word vocabulary in order to control the model complexity in state-of-the-art neural machine translation (NMT) systems is an important bottleneck on performance, especially for morphologically rich languages. Conventional methods that aim to overcome this problem by using sub-word or character-level representations solely rely on statistics and disregard the linguistic properties of words, which leads to interruptions in the word structure and causes semantic and syntactic losses. In this paper, we propose a new vocabulary reduction method for NMT, which can reduce the vocabulary of a given input corpus at any rate while also considering the morphological properties of the language. Our method is based on unsupervised morphology learning and can be, in principle, used for pre-processing any language pair. We also present an alternative word segmentation method based on supervised morphological analysis, which aids us in measuring the accuracy of our model. We evaluate our method in Turkish-to-English NMT task where the input language is morphologically rich and agglutinative. We analyze different representation methods in terms of translation accuracy as well as the semantic and syntactic properties of the generated output. Our method obtains a significant improvement of 2.3 BLEU points over the conventional vocabulary reduction technique, showing that it can provide better accuracy in open vocabulary translation of morphologically rich languages.

Connected through hyperedge HE-1 (induced by source paper "morphological word segmentation on agglutinative languages for neural machine translation"):
  with members:
    - "sockeye a toolkit for neural machine translation"
    - "attention is all you need"
    - "target side context for discriminative models in statistical machine translation"
    - "improving neural machine translation models with monolingual data"
    - "effective approaches to attention based neural machine translation"
    - "neural machine translation of rare words with subword units"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-2 (induced by source paper "a latent morphology model for open vocabulary neural machine translation"):
  with members:
    - "on the importance of word boundaries in character level neural machine translation"
    - "interpretable neural predictions with differentiable binary variables"
    - "revisiting character based neural machine translation with capacity and compression"
    - "learning to segment inputs for nmt favors character level processing"
    - "are all languages equally hard to language model"
    - "character level models versus morphology in semantic role labeling"
    - "from characters to words to in between do we capture morphology"
    - "opennmt open source toolkit for neural machine translation"

Connected through hyperedge HE-3 (induced by source paper "on the importance of word boundaries in character level neural machine translation"):
  with members:
    - "revisiting character based neural machine translation with capacity and compression"
    - "learning to segment inputs for nmt favors character level processing"
    - "attention is all you need"
    - "opennmt open source toolkit for neural machine translation"
    - "deep architectures for neural machine translation"
    - "achieving open vocabulary neural machine translation with hybrid word character models"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "character based neural machine translation"

Connected through hyperedge HE-4 (induced by source paper "finding better subword segmentation for neural machine translation"):
  with members:
    - "compositional representation of morphologically rich input for neural machine translation"
    - "fast and accurate neural word segmentation for chinese"
    - "character based neural machine translation"
    - "neural word segmentation learning for chinese"
    - "achieving open vocabulary neural machine translation with hybrid word character models"
    - "fully character level neural machine translation without explicit segmentation"
    - "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"

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"
    - "improving character based decoding using target side morphological information for neural machine translation"
    - "lifelong learning with dynamically expandable networks"
    - "modeling target side inflection in neural machine translation"
    - "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 "compositional representation of morphologically rich input for neural machine translation"):
  with members:
    - "from characters to words to in between do we capture morphology"
    - "pre translation for neural machine translation"
    - "achieving open vocabulary neural machine translation with hybrid word character models"
    - "fully character level neural machine translation without explicit segmentation"
    - "theano a python framework for fast computation of mathematical expressions"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "character based neural machine translation"
    - "neural machine translation of rare words with subword units"

Connected through hyperedge HE-7 (induced by source paper "modeling target side inflection in neural machine translation"):
  with members:
    - "syntax aware neural machine translation using ccg"
    - "improving neural machine translation models with monolingual data"
    - "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)