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: "exploiting sentential context for neural machine translation"
  Abstract: In this work, we present novel approaches to exploit sentential context for neural machine translation (NMT). Specifically, we first show that a shallow sentential context extracted from the top encoder layer only, can improve translation performance via contextualizing the encoding representations of individual words. Next, we introduce a deep sentential context, which aggregates the sentential context representations from all the internal layers of the encoder to form a more comprehensive context representation. Experimental results on the WMT14 English-to-German and English-to-French benchmarks show that our model consistently improves performance over the strong TRANSFORMER model (Vaswani et al., 2017), demonstrating the necessity and effectiveness of exploiting sentential context for NMT.

Connected through hyperedge HE-1 (induced by source paper "self attention with structural position representations"):
  with members:
    - "multi layer representation fusion for neural machine translation"
    - "towards better modeling hierarchical structure for self attention with ordered neurons"
    - "modeling recurrence for transformer"
    - "semantic neural machine translation using amr"
    - "adaptive attention span in transformers"
    - "exploiting deep representations for neural machine translation"
    - "self attention with relative position representations"
    - "modeling localness for self attention networks"

Connected through hyperedge HE-2 (induced by source paper "towards better modeling hierarchical structure for self attention with ordered neurons"):
  with members:
    - "transformers with convolutional context for asr"
    - "self attention with structural position representations"
    - "modeling recurrence for transformer"
    - "adaptive attention span in transformers"
    - "exploiting deep representations for neural machine translation"
    - "modeling localness for self attention networks"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-3 (induced by source paper "explicit sentence compression for neural machine translation"):
  with members:
    - "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"
    - "unsupervised machine translation using monolingual corpora only"

Connected through hyperedge HE-4 (induced by source paper "coursera corpus mining and multistage fine tuning for improving lectures translation"):
  with members:
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "target conditioned sampling optimizing data selection for multilingual neural machine translation"
    - "exploiting out of domain parallel data through multilingual transfer learning for low resource neural machine translation"
    - "when a good translation is wrong in context context aware machine translation improves on deixis ellipsis and lexical cohesion"
    - "attention is all you need"
    - "an empirical comparison of simple domain adaptation methods for neural machine translation"
    - "neural machine translation"
    - "six challenges for neural machine translation"

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)