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: "unfolding and shrinking neural machine translation ensembles"
  Abstract: Ensembling is a well-known technique in neural machine translation (NMT) to improve system performance. Instead of a single neural net, multiple neural nets with the same topology are trained separately, and the decoder generates predictions by averaging over the individual models. Ensembling often improves the quality of the generated translations drastically. However, it is not suitable for production systems because it is cumbersome and slow. This work aims to reduce the runtime to be on par with a single system without compromising the translation quality. First, we show that the ensemble can be unfolded into a single large neural network which imitates the output of the ensemble system. We show that unfolding can already improve the runtime in practice since more work can be done on the GPU. We proceed by describing a set of techniques to shrink the unfolded network by reducing the dimensionality of layers. On Japanese-English we report that the resulting network has the size and decoding speed of a single NMT network but performs on the level of a 3-ensemble system.

Connected through hyperedge HE-1 (induced by source paper "in training matrix factorization for parameter frugal neural machine translation"):
  with members:
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "training tips for the transformer model"
    - "pieces of eight 8 bit neural machine translation"
    - "attention is all you need"
    - "sharp models on dull hardware fast and accurate neural machine translation decoding on the cpu"
    - "opennmt open source toolkit for neural machine translation"
    - "factorization tricks for lstm networks"
    - "mixed precision training"

Connected through hyperedge HE-2 (induced by source paper "why not be versatile applications of the sgnmt decoder for machine translation"):
  with members:
    - "attention is all you need"
    - "opennmt open source toolkit for neural machine translation"
    - "sgnmt a flexible nmt decoding platform for quick prototyping of new models and search strategies"
    - "is neural machine translation ready for deployment a case study on 30 translation directions"
    - "neural machine translation by minimising the bayes risk with respect to syntactic translation lattices"
    - "syntactically guided neural machine translation"
    - "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-3 (induced by source paper "distilling knowledge for search based structured prediction"):
  with members:
    - "opennmt open source toolkit for neural machine translation"
    - "reporting score distributions makes a difference performance study of lstm networks for sequence tagging"
    - "sequence to sequence learning as beam search optimization"
    - "deep exploration via bootstrapped dqn"
    - "globally normalized transition based neural networks"
    - "training with exploration improves a greedy stack lstm parser"
    - "distilling an ensemble of greedy dependency parsers into one mst parser"
    - "what do recurrent neural network grammars learn about syntax"

Connected through hyperedge HE-4 (induced by source paper "iea inner ensemble average within a convolutional neural network"):
  with members:
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "aggregated residual transformations for deep neural networks"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "identity mappings in deep residual networks"
    - "squad 100 000 questions for machine comprehension of text"
    - "densely connected convolutional 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)