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: "multi channel encoder for neural machine translation"
  Abstract: Attention-based Encoder-Decoder has the effective architecture for neural machine translation (NMT), which typically relies on recurrent neural networks (RNN) to build the blocks that will be lately called by attentive reader during the decoding process. This design of encoder yields relatively uniform composition on source sentence, despite the gating mechanism employed in encoding RNN. On the other hand, we often hope the decoder to take pieces of source sentence at varying levels suiting its own linguistic structure: for example, we may want to take the entity name in its raw form while taking an idiom as a perfectly composed unit. Motivated by this demand, we propose Multi-channel Encoder (MCE), which enhances encoding components with different levels of composition. More specifically, in addition to the hidden state of encoding RNN, MCE takes 1) the original word embedding for raw encoding with no composition, and 2) a particular design of external memory in Neural Turing Machine (NTM) for more complex composition, while all three encoding strategies are properly blended during decoding. Empirical study on Chinese-English translation shows that our model can improve by 6.52 BLEU points upon a strong open source NMT system: DL4MT1. On the WMT14 English- French task, our single shallow system achieves BLEU=38.8, comparable with the state-of-the-art deep models.

Connected through hyperedge HE-1 (induced by source paper "multi layer representation fusion for neural machine translation"):
  with members:
    - "path aggregation network for instance segmentation"
    - "towards neural phrase based machine translation"
    - "attention is all you need"
    - "stronger baselines for trustable results in neural machine translation"
    - "classical structured prediction losses for sequence to sequence learning"
    - "convolutional sequence to sequence learning"
    - "deep neural machine translation with linear associative unit"
    - "a structured self attentive sentence embedding"

Connected through hyperedge HE-2 (induced by source paper "closed book training to improve summarization encoder memory"):
  with members:
    - "improved image captioning via policy gradient optimization of spider"
    - "efficient summarization with read again and copy mechanism"
    - "get to the point summarization with pointer generator networks"
    - "a deep reinforced model for abstractive summarization"
    - "pointing the unknown words"
    - "memory enhanced decoder for neural machine translation"
    - "abstractive text summarization using sequence to sequence rnns and beyond"
    - "incorporating copying mechanism in sequence to sequence learning"

Connected through hyperedge HE-3 (induced by source paper "unsupervised neural machine translation with weight sharing"):
  with members:
    - "unsupervised machine translation using monolingual corpora only"
    - "learning to remember translation history with a continuous cache"
    - "attention is all you need"
    - "improving neural machine translation with conditional sequence generative adversarial nets"
    - "convolutional sequence to sequence learning"
    - "deep neural machine translation with linear associative unit"
    - "disan directional self attention network for rnn cnn free language understanding"
    - "a correlational encoder decoder architecture for pivot based sequence generation"

Connected through hyperedge HE-4 (induced by source paper "future prediction based model for neural machine translation"):
  with members:
    - "decoding history based adaptive control of attention for neural machine translation"
    - "bag of words as target for neural machine translation"
    - "learning when to concentrate or divert attention self adaptive attention temperature for neural machine translation"
    - "attention is all you need"
    - "coverage embedding models for neural machine translation"
    - "interactive attention for neural machine translation"
    - "neural machine translation in linear time"
    - "modeling coverage for neural machine translation"

Connected through hyperedge HE-5 (induced by source paper "a task in a suit and a tie paraphrase generation with semantic augmentation"):
  with members:
    - "exploiting semantics in neural machine translation with graph convolutional networks"
    - "building a conversational agent overnight with dialogue self play"
    - "linguistically informed self attention for semantic role labeling"
    - "a continuously growing dataset of sentential paraphrases"
    - "attention is all you need"
    - "sling a framework for frame semantic parsing"
    - "a deep generative framework for paraphrase generation"
    - "neural paraphrase generation with stacked residual lstm networks"

Connected through hyperedge HE-6 (induced by source paper "double path networks for sequence to sequence learning"):
  with members:
    - "syntax directed attention for neural machine translation"
    - "attention is all you need"
    - "convolutional sequence to sequence learning"
    - "neural headline generation with minimum risk training"
    - "language modeling with gated convolutional networks"
    - "an actor critic algorithm for sequence prediction"
    - "a neural attention model for abstractive sentence summarization"
    - "effective approaches to attention based neural machine translation"

Connected through hyperedge HE-7 (induced by source paper "mcrm mother compact recurrent memory"):
  with members:
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "deep voice real time neural text to speech"
    - "object detection in videos with tubelet proposal networks"
    - "architectural complexity measures of recurrent neural networks"
    - "unitary evolution recurrent neural networks"
    - "grid long short term memory"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-8 (induced by source paper "automatic transferring between ancient chinese and contemporary chinese"):
  with members:
    - "bag of words as target for neural machine translation"
    - "doubly attentive decoder for multi modal neural machine translation"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "modeling coverage for neural machine translation"
    - "lattice based recurrent neural network encoders for neural machine translation"
    - "effective approaches to attention based neural machine translation"
    - "neural machine translation by jointly learning to align and translate"
    - "on using very large target vocabulary 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)