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: "neural machine translation via binary code prediction"
  Abstract: In this paper, we propose a new method for calculating the output layer in neural machine translation systems. The method is based on predicting a binary code for each word and can reduce computation time/memory requirements of the output layer to be logarithmic in vocabulary size in the best case. In addition, we also introduce two advanced approaches to improve the robustness of the proposed model: using error-correcting codes and combining softmax and binary codes. Experiments on two English-Japanese bidirectional translation tasks show proposed models achieve BLEU scores that approach the softmax, while reducing memory usage to the order of less than 1/10 and improving decoding speed on CPUs by x5 to x10.

Connected through hyperedge HE-1 (induced by source paper "error correcting neural sequence prediction"):
  with members:
    - "breaking the softmax bottleneck a high rank rnn language model"
    - "differentiable scheduled sampling for credit assignment"
    - "compressing word embeddings via deep compositional code learning"
    - "efficient softmax approximation for gpus"
    - "the concrete distribution a continuous relaxation of discrete random variables"
    - "strategies for training large vocabulary neural language models"
    - "scheduled sampling for sequence prediction with recurrent neural networks"
    - "rethinking the inception architecture for computer vision"

Connected through hyperedge HE-2 (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"
    - "ensemble distillation for neural machine translation"
    - "neural machine translation by jointly learning to align and translate"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-3 (induced by source paper "accelerated reinforcement learning for sentence generation by vocabulary prediction"):
  with members:
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "subword regularization improving neural network translation models with multiple subword candidates"
    - "learning to parse and translate improves neural machine translation"
    - "neural machine translation with source side latent graph parsing"
    - "neural machine translation with word predictions"
    - "improving neural machine translation with conditional sequence generative adversarial nets"
    - "a deep reinforced model for abstractive summarization"
    - "tree to sequence attentional neural machine translation"

Connected through hyperedge HE-4 (induced by source paper "von mises fisher loss for training sequence to sequence models with continuous outputs"):
  with members:
    - "billion scale similarity search with gpus"
    - "mimicking word embeddings using subword rnns"
    - "stronger baselines for trustable results in neural machine translation"
    - "get to the point summarization with pointer generator networks"
    - "enriching word vectors with subword information"
    - "using the output embedding to improve language models"
    - "a neural attention model for abstractive sentence summarization"
    - "effective approaches to attention based 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"
    - "chunk based bi scale decoder for neural machine translation"
    - "convolutional sequence to sequence learning"
    - "depthwise separable convolutions for 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)