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: "attention focusing for neural machine translation by bridging source and target embeddings"
  Abstract: In neural machine translation, a source sequence of words is encoded into a vector from which a target sequence is generated in the decoding phase. Differently from statistical machine translation, the associations between source words and their possible target counterparts are not explicitly stored. Source and target words are at the two ends of a long information processing procedure, mediated by hidden states at both the source encoding and the target decoding phases. This makes it possible that a source word is incorrectly translated into a target word that is not any of its admissible equivalent counterparts in the target language. #R##N#In this paper, we seek to somewhat shorten the distance between source and target words in that procedure, and thus strengthen their association, by means of a method we term bridging source and target word embeddings. We experiment with three strategies: (1) a source-side bridging model, where source word embeddings are moved one step closer to the output target sequence; (2) a target-side bridging model, which explores the more relevant source word embeddings for the prediction of the target sequence; and (3) a direct bridging model, which directly connects source and target word embeddings seeking to minimize errors in the translation of ones by the others. #R##N#Experiments and analysis presented in this paper demonstrate that the proposed bridging models are able to significantly improve quality of both sentence translation, in general, and alignment and translation of individual source words with target words, in particular.

Connected through hyperedge HE-1 (induced by source paper "controlling utterance length in nmt based word segmentation with attention"):
  with members:
    - "attention is not not explanation"
    - "attention is not explanation"
    - "tied multitask learning for neural speech translation"
    - "learning to segment inputs for nmt favors character level processing"
    - "low resource speech to text translation"
    - "on the alignment problem in multi head attention based neural machine translation"
    - "pre training on high resource speech recognition improves low resource speech to text translation"
    - "subword regularization improving neural network translation models with multiple subword candidates"

Connected through hyperedge HE-2 (induced by source paper "interrogating the explanatory power of attention in neural machine translation"):
  with members:
    - "saliency driven word alignment interpretation for neural machine translation"
    - "model explanations under calibration"
    - "an analysis of attention over clinical notes for predictive tasks"
    - "analyzing the structure of attention in a transformer language model"
    - "attention is not explanation"
    - "do transformer attention heads provide transparency in abstractive summarization"
    - "what does bert look at an analysis of bert s attention"
    - "interpreting recurrent and attention based neural models a case study on natural language inference"

Connected through hyperedge HE-3 (induced by source paper "shared private bilingual word embeddings for neural machine translation"):
  with members:
    - "achieving human parity on automatic chinese to english news translation"
    - "universal neural machine translation for extremely low resource languages"
    - "compositional representation of morphologically rich input for neural machine translation"
    - "the best of both worlds combining recent advances in neural machine translation"
    - "thumt an open source toolkit for neural machine translation"
    - "the university of edinburgh s neural mt systems for wmt17"
    - "attention is all you need"
    - "slim embedding layers for recurrent neural language models"

Connected through hyperedge HE-4 (induced by source paper "widening the representation bottleneck in neural machine translation with lexical shortcuts"):
  with members:
    - "universal transformers"
    - "exploiting deep representations for neural machine translation"
    - "tensor2tensor for neural machine translation"
    - "why self attention a targeted evaluation of neural machine translation architectures"
    - "evaluating layers of representation in neural machine translation on part of speech and semantic tagging tasks"
    - "a call for clarity in reporting bleu scores"
    - "training deeper neural machine translation models with transparent attention"
    - "multi representation ensembles and delayed sgd updates improve syntax based nmt"

Connected through hyperedge HE-5 (induced by source paper "understanding neural machine translation by simplification the case of encoder free models"):
  with members:
    - "adding interpretable attention to neural translation models improves word alignment"
    - "on the evaluation of semantic phenomena in neural machine translation using natural language inference"
    - "evaluating layers of representation in neural machine translation on part of speech and semantic tagging tasks"
    - "a call for clarity in reporting bleu scores"
    - "an analysis of attention mechanisms the case of word sense disambiguation in neural machine translation"
    - "sockeye a toolkit for neural machine translation"
    - "attention is all you need"
    - "what does attention in neural machine translation pay attention to"

Connected through hyperedge HE-6 (induced by source paper "induction networks for few shot text classification"):
  with members:
    - "analogical reasoning on chinese morphological and semantic relations"
    - "lifelong domain word embedding via meta learning"
    - "investigating capsule networks with dynamic routing for text classification"
    - "zero shot user intent detection via capsule neural networks"
    - "diverse few shot text classification with multiple metrics"
    - "low shot learning with imprinted weights"
    - "prototypical networks for few shot learning"
    - "dynamic routing between capsules"

Connected through hyperedge HE-7 (induced by source paper "korean to chinese machine translation using chinese character as pivot clue"):
  with members:
    - "semantic role labeling with associated memory network"
    - "dependency or span end to end uniform semantic role labeling"
    - "lattice based transformer encoder for neural machine translation"
    - "opennmt neural machine translation toolkit"
    - "finding better subword segmentation for neural machine translation"
    - "exploring recombination for efficient decoding of neural machine translation"
    - "attention is all you need"
    - "fast and accurate neural word segmentation for chinese"

Connected through hyperedge HE-8 (induced by source paper "fusionstitching boosting execution efficiency of memory intensive computations for dl workloads"):
  with members:
    - "billion scale commodity embedding for e commerce recommendation in alibaba"
    - "optimal dnn primitive selection with partitioned boolean quadratic programming"
    - "deep residual learning for image recognition"
    - "mxnet a flexible and efficient machine learning library for heterogeneous distributed systems"
    - "rethinking the inception architecture for computer vision"
    - "efficient kernel fusion techniques for massive video data analysis on gpgpus"
    - "very deep convolutional networks for large scale image recognition"

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)