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: "a coverage embedding model for neural machine translation"
  Abstract: In this paper, we enhance the attention-based neural machine translation by adding an explicit coverage embedding model to alleviate issues of repeating and dropping translations in NMT. For each source word, our model starts with a full coverage embedding vector, and then keeps updating it with a gated recurrent unit as the translation goes. All the initialized coverage embeddings and updating matrix are learned in the training procedure. Experiments on the large-scale Chineseto-English task show that our enhanced model improves the translation quality significantly on various test sets over the strong large vocabulary NMT system.

Connected through hyperedge HE-1 (induced by source paper "apply chinese radicals into neural machine translation deeper than character level"):
  with members:
    - "improving character level japanese chinese neural machine translation with radicals as an additional input feature"
    - "imagination improves multimodal translation"
    - "word subword or character an empirical study of granularity in chinese english nmt"
    - "neural machine translation and sequence to sequence models a tutorial"
    - "multilingual multi modal embeddings for natural language processing"
    - "towards string to tree neural machine translation"
    - "learning character level compositionality with visual features"
    - "six challenges for neural machine translation"

Connected through hyperedge HE-2 (induced by source paper "incorporating chinese radicals into neural machine translation deeper than character level"):
  with members:
    - "lepor an augmented machine translation evaluation metric"
    - "neural machine translation and sequence to sequence models a tutorial"
    - "learning character level compositionality with visual features"
    - "six challenges for neural machine translation"
    - "context gates for neural machine translation"
    - "a character level decoder without explicit segmentation for neural machine translation"
    - "effective approaches to attention based neural machine translation"
    - "neural machine translation of rare words with subword units"

Connected through hyperedge HE-3 (induced by source paper "phrase table as recommendation memory for neural machine translation"):
  with members:
    - "translating phrases in neural machine translation"
    - "memory augmented neural machine translation"
    - "neural machine translation advised by statistical machine translation"
    - "is neural machine translation ready for deployment a case study on 30 translation directions"
    - "implicit distortion and fertility models for attention based encoder decoder nmt model"
    - "bridging neural machine translation and bilingual dictionaries"
    - "vocabulary manipulation for neural machine translation"
    - "the amu uedin submission to the wmt16 news translation task attention based nmt models as feature functions in phrase based smt"

Connected through hyperedge HE-4 (induced by source paper "a unified query based generative model for question generation and question answering"):
  with members:
    - "jointly optimize data augmentation and network training adversarial data augmentation in human pose estimation"
    - "neural models for key phrase detection and question generation"
    - "learning to ask neural question generation for reading comprehension"
    - "a joint model for question answering and question generation"
    - "end to end answer chunk extraction and ranking for reading comprehension"
    - "learning recurrent span representations for extractive question answering"
    - "neural question generation from text a preliminary study"
    - "get to the point summarization with pointer generator networks"

Connected through hyperedge HE-5 (induced by source paper "look ahead attention for generation in neural machine translation"):
  with members:
    - "neural system combination for machine translation"
    - "attention is all you need"
    - "six challenges for neural machine translation"
    - "a structured self attentive sentence embedding"
    - "a deep reinforced model for abstractive summarization"
    - "neural machine translation advised by statistical machine translation"
    - "is neural machine translation ready for deployment a case study on 30 translation directions"
    - "context gates for neural machine translation"

Connected through hyperedge HE-6 (induced by source paper "memory augmented neural machine translation"):
  with members:
    - "flexible and creative chinese poetry generation using neural memory"
    - "memory enhanced decoder for neural machine translation"
    - "neural versus phrase based machine translation quality a case study"
    - "syntactically guided neural machine translation"
    - "incorporating structural alignment biases into an attentional neural translation model"
    - "incorporating discrete translation lexicons into neural machine translation"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "google s multilingual neural machine translation system enabling zero shot translation"

Connected through hyperedge HE-7 (induced by source paper "towards better decoding and language model integration in sequence to sequence models"):
  with members:
    - "very deep convolutional networks for end to end speech recognition"
    - "sequence to sequence learning as beam search optimization"
    - "disturblabel regularizing cnn on the loss layer"
    - "learning online alignments with continuous rewards policy gradient"
    - "globally normalized transition based neural networks"
    - "asynchronous methods for deep reinforcement learning"
    - "lip reading sentences in the wild"
    - "modeling coverage for neural machine translation"

Connected through hyperedge HE-8 (induced by source paper "bridging neural machine translation and bilingual dictionaries"):
  with members:
    - "character based neural machine translation"
    - "is neural machine translation ready for deployment a case study on 30 translation directions"
    - "implicit distortion and fertility models for attention based encoder decoder nmt model"
    - "neural machine translation with supervised attention"
    - "interactive attention for neural machine translation"
    - "semi supervised learning for neural machine translation"
    - "fully character level neural machine translation without explicit segmentation"
    - "incorporating structural alignment biases into an attentional neural translation model"

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)