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: "ensemble distillation for neural machine translation"
  Abstract: Knowledge distillation describes a method for training a student network to perform better by learning from a stronger teacher network. Translating a sentence with an Neural Machine Translation (NMT) engine is time expensive and having a smaller model speeds up this process. We demonstrate how to transfer the translation quality of an ensemble and an oracle BLEU teacher network into a single NMT system. Further, we present translation improvements from a teacher network that has the same architecture and dimensions of the student network. As the training of the student model is still expensive, we introduce a data filtering method based on the knowledge of the teacher model that not only speeds up the training, but also leads to better translation quality. Our techniques need no code change and can be easily reproduced with any NMT architecture to speed up the decoding process.

Connected through hyperedge HE-1 (induced by source paper "multilingual neural machine translation with knowledge distillation"):
  with members:
    - "knowledge distillation by on the fly native ensemble"
    - "sentence wise smooth regularization for sequence to sequence learning"
    - "universal neural machine translation for extremely low resource languages"
    - "meta learning for low resource neural machine translation"
    - "large scale distributed neural network training through online distillation"
    - "when and why are pre trained word embeddings useful for neural machine translation"
    - "non autoregressive neural machine translation with enhanced decoder input"
    - "double path networks for sequence to sequence learning"

Connected through hyperedge HE-2 (induced by source paper "token level ensemble distillation for grapheme to phoneme conversion"):
  with members:
    - "fastspeech fast robust and controllable text to speech"
    - "multilingual neural machine translation with knowledge distillation"
    - "mass masked sequence to sequence pre training for language generation"
    - "the evolved transformer"
    - "universal transformers"
    - "achieving human parity on automatic chinese to english news translation"
    - "attending to mathematical language with transformers"
    - "sentence wise smooth regularization for sequence to sequence learning"

Connected through hyperedge HE-3 (induced by source paper "end to end speech translation with knowledge distillation"):
  with members:
    - "multilingual neural machine translation with knowledge distillation"
    - "augmenting librispeech with french translations a multimodal corpus for direct speech translation evaluation"
    - "end to end automatic speech translation of audiobooks"
    - "pre training on high resource speech recognition improves low resource speech to text translation"
    - "large scale distributed neural network training through online distillation"
    - "knowledge distillation in generations more tolerant teachers educate better students"
    - "sequence to sequence models can directly translate foreign speech"
    - "attention is all you need"

Connected through hyperedge HE-4 (induced by source paper "balancing cost and benefit with tied multi transformers"):
  with members:
    - "multi layer representation fusion for neural machine translation"
    - "exploiting deep representations for neural machine translation"
    - "recurrent stacking of layers for compact neural machine translation models"
    - "scaling neural machine translation"
    - "evaluating layers of representation in neural machine translation on part of speech and semantic tagging tasks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a call for clarity in reporting bleu scores"
    - "accelerating neural transformer via an average attention network"

Connected through hyperedge HE-5 (induced by source paper "multi agent learning for neural machine translation"):
  with members:
    - "pay less attention with lightweight and dynamic convolutions"
    - "synchronous bidirectional inference for neural sequence generation"
    - "asynchronous bidirectional decoding for neural machine translation"
    - "regularizing neural machine translation by target bidirectional agreement"
    - "self attention with relative position representations"
    - "why self attention a targeted evaluation of neural machine translation architectures"
    - "understanding back translation at scale"
    - "attention is all you need"

Connected through hyperedge HE-6 (induced by source paper "multi layer softmaxing during training neural machine translation for flexible decoding with fewer layers"):
  with members:
    - "multi layer representation fusion for neural machine translation"
    - "exploiting deep representations for neural machine translation"
    - "evaluating layers of representation in neural machine translation on part of speech and semantic tagging tasks"
    - "accelerating neural transformer via an average attention network"
    - "attention is all you need"
    - "neural machine translation via binary code prediction"
    - "neural machine translation by jointly learning to align and translate"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-7 (induced by source paper "pieces of eight 8 bit neural machine translation"):
  with members:
    - "sharp models on dull hardware fast and accurate neural machine translation decoding on the cpu"
    - "six challenges for neural machine translation"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "adam a method for stochastic optimization"
    - "on the properties of neural machine translation encoder decoder approaches"
    - "neural machine translation by jointly learning to align and translate"
    - "sequence to sequence learning with neural networks"
    - "kblas an optimized library for dense matrix vector multiplication on gpu accelerators"

Connected through hyperedge HE-8 (induced by source paper "unfolding and shrinking neural machine translation ensembles"):
  with members:
    - "qcri machine translation systems for iwslt 16"
    - "sgnmt a flexible nmt decoding platform for quick prototyping of new models and search strategies"
    - "noiseout a simple way to prune neural networks"
    - "learning compact recurrent neural networks"
    - "edinburgh neural machine translation systems for wmt 16"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "learning both weights and connections for efficient neural networks"
    - "neural reranking improves subjective quality of machine translation naist at wat2015"

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)