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: "chunk based bi scale decoder for neural machine translation"
  Abstract: In typical neural machine translation~(NMT), the decoder generates a sentence word by word, packing all linguistic granularities in the same time-scale of RNN. In this paper, we propose a new type of decoder for NMT, which splits the decode state into two parts and updates them in two different time-scales. Specifically, we first predict a chunk time-scale state for phrasal modeling, on top of which multiple word time-scale states are generated. In this way, the target sentence is translated hierarchically from chunks to words, with information in different granularities being leveraged. Experiments show that our proposed model significantly improves the translation performance over the state-of-the-art NMT model.

Connected through hyperedge HE-1 (induced by source paper "syntax enhanced neural machine translation with syntax aware word representations"):
  with members:
    - "towards robust neural machine translation"
    - "deep contextualized word representations"
    - "structural attention neural networks for improved sentiment analysis"
    - "towards bidirectional hierarchical representations for attention based neural machine translation"
    - "attention is all you need"
    - "neural machine translation with source side latent graph parsing"
    - "stronger baselines for trustable results in neural machine translation"
    - "modeling past and future for neural machine translation"

Connected through hyperedge HE-2 (induced by source paper "graph based bilingual word embedding for statistical machine translation"):
  with members:
    - "syntax directed attention for neural machine translation"
    - "mimicking word embeddings using subword rnns"
    - "offline bilingual word vectors orthogonal transformations and the inverted softmax"
    - "a simple regularization based algorithm for learning cross domain word embeddings"
    - "six challenges for neural machine translation"
    - "bilingual learning of multi sense embeddings with discrete autoencoders"
    - "cross lingual models of word embeddings an empirical comparison"
    - "vocabulary manipulation for neural machine translation"

Connected through hyperedge HE-3 (induced by source paper "translating pro drop languages with reconstruction models"):
  with members:
    - "translating phrases in neural machine translation"
    - "learning to remember translation history with a continuous cache"
    - "exploiting cross sentence context for neural machine translation"
    - "modeling source syntax for neural machine translation"
    - "search engine guided non parametric neural machine translation"
    - "neural machine translation advised by statistical machine translation"
    - "context gates for neural machine translation"
    - "neural machine translation with reconstruction"

Connected through hyperedge HE-4 (induced by source paper "learning to remember translation history with a continuous cache"):
  with members:
    - "translating pro drop languages with reconstruction models"
    - "translating phrases in neural machine translation"
    - "frustratingly short attention spans in neural language modeling"
    - "exploiting cross sentence context for neural machine translation"
    - "learning to remember rare events"
    - "modeling source syntax for neural machine translation"
    - "learning to create and reuse words in open vocabulary neural language modeling"
    - "six challenges for neural machine translation"

Connected through hyperedge HE-5 (induced by source paper "non autoregressive neural machine translation"):
  with members:
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"
    - "fast decoding in sequence models using discrete latent variables"
    - "attention is all you need"
    - "parallel wavenet fast high fidelity speech synthesis"
    - "neural machine translation via binary code prediction"
    - "convolutional sequence to sequence learning"
    - "depthwise separable convolutions for neural machine translation"
    - "six challenges for neural machine translation"

Connected through hyperedge HE-6 (induced by source paper "coupling distributed and symbolic execution for natural language queries"):
  with members:
    - "language to logical form with neural attention"
    - "harnessing deep neural networks with logic rules"
    - "neural symbolic machines learning semantic parsers on freebase with weak supervision"
    - "a network based end to end trainable task oriented dialogue system"
    - "coverage embedding models for neural machine translation"
    - "rationalizing neural predictions"
    - "neural programmer inducing latent programs with gradient descent"
    - "neural generative question answering"

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)