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: "overcoming the curse of sentence length for neural machine translation using automatic segmentation"
  Abstract: The authors of (Cho et al., 2014a) have shown that the recently introduced neural network translation systems suffer from a significant drop in translation quality when translating long sentences, unlike existing phrase-based translation systems. In this paper, we propose a way to address this issue by automatically segmenting an input sentence into phrases that can be easily translated by the neural network translation model. Once each segment has been independently translated by the neural machine translation model, the translated clauses are concatenated to form a final translation. Empirical results show a significant improvement in translation quality for long sentences.

Connected through hyperedge HE-1 (induced by source paper "context in neural machine translation a review of models and evaluations"):
  with members:
    - "attaining the unattainable reassessing claims of human parity in neural machine translation"
    - "assessing crosslingual discourse relations in machine translation"
    - "pronoun translation in english french machine translation an analysis of error types"
    - "why self attention a targeted evaluation of neural machine translation architectures"
    - "marian fast neural machine translation in c"
    - "automatic reference based evaluation of pronoun translation misses the point"
    - "context aware neural machine translation learns anaphora resolution"
    - "a comparison of transformer and recurrent neural networks on multilingual neural machine translation"

Connected through hyperedge HE-2 (induced by source paper "explicit sentence compression for neural machine translation"):
  with members:
    - "exploiting sentential context for neural machine translation"
    - "mass masked sequence to sequence pre training for language generation"
    - "exploiting deep representations for neural machine translation"
    - "unsupervised sentence compression using denoising auto encoders"
    - "modeling localness for self attention networks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "learning to encode text as human readable summaries using generative adversarial networks"
    - "phrase based neural unsupervised machine translation"

Connected through hyperedge HE-3 (induced by source paper "neural machine translation on scarce resource condition a case study on persian english"):
  with members:
    - "guided alignment training for topic aware neural machine translation"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "on the properties of neural machine translation encoder decoder approaches"
    - "neural machine translation by jointly learning to align and translate"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "sequence to sequence learning with neural networks"
    - "adadelta an adaptive learning rate method"
    - "multi column deep neural networks for image classification"

Connected through hyperedge HE-4 (induced by source paper "machine translation at booking com journey and lessons learned"):
  with members:
    - "opennmt open source toolkit for neural machine translation"
    - "is neural machine translation ready for deployment a case study on 30 translation directions"
    - "systran s pure neural machine translation systems"
    - "effective approaches to attention based neural machine translation"
    - "on the properties of neural machine translation encoder decoder approaches"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-5 (induced by source paper "length bias in encoder decoder models and a case for global conditioning"):
  with members:
    - "globally normalized transition based neural networks"
    - "smart reply automated response suggestion for email"
    - "conversational contextual cues the case of personalization and history for response ranking"
    - "the ubuntu dialogue corpus a large dataset for research in unstructured multi turn dialogue systems"
    - "quantization based fast inner product search"
    - "sequence level training with recurrent neural networks"
    - "scheduled sampling for sequence prediction with recurrent neural networks"
    - "a neural conversational model"

Connected through hyperedge HE-6 (induced by source paper "tree to sequence attentional neural machine translation"):
  with members:
    - "incorporating structural alignment biases into an attentional neural translation model"
    - "multi source neural translation"
    - "neural reranking improves subjective quality of machine translation naist at wat2015"
    - "effective approaches to attention based neural machine translation"
    - "agreement based joint training for bidirectional attention based neural machine translation"
    - "improved semantic representations from tree structured long short term memory networks"
    - "blackout speeding up recurrent neural network language models with very large vocabularies"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-7 (induced by source paper "a survey of voice translation methodologies acoustic dialect decoder"):
  with members:
    - "a critical review of recurrent neural networks for sequence learning"
    - "on the properties of neural machine translation encoder decoder approaches"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "neural machine translation by jointly learning to align and translate"
    - "on using very large target vocabulary for neural machine translation"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "sequence to sequence learning with neural networks"
    - "addressing the rare word problem in neural machine translation"

Connected through hyperedge HE-8 (induced by source paper "neural versus phrase based machine translation quality a case study"):
  with members:
    - "neural reranking improves subjective quality of machine translation naist at wat2015"
    - "effective approaches to attention based neural machine translation"
    - "neural machine translation by jointly learning to align and translate"
    - "sequence to sequence learning with neural networks"

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)