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: "phrase table as recommendation memory for neural machine translation"
  Abstract: Neural Machine Translation (NMT) has drawn much attention due to its promising translation performance recently. However, several studies indicate that NMT often generates fluent but unfaithful translations. In this paper, we propose a method to alleviate this problem by using a phrase table as recommendation memory. The main idea is to add bonus to words worthy of recommendation, so that NMT can make correct predictions. Specifically, we first derive a prefix tree to accommodate all the candidate target phrases by searching the phrase translation table according to the source sentence. Then, we construct a recommendation word set by matching between candidate target phrases and previously translated target words by NMT. After that, we determine the specific bonus value for each recommendable word by using the attention vector and phrase translation probability. Finally, we integrate this bonus value into NMT to improve the translation results. The extensive experiments demonstrate that the proposed methods obtain remarkable improvements over the strong attentionbased NMT.

Connected through hyperedge HE-1 (induced by source paper "multi granularity self attention for neural machine translation"):
  with members:
    - "analyzing multi head self attention specialized heads do the heavy lifting the rest can be pruned"
    - "area attention"
    - "information aggregation for multi head attention with routing by agreement"
    - "strategies for structuring story generation"
    - "towards better modeling hierarchical structure for self attention with ordered neurons"
    - "syntax enhanced neural machine translation with syntax aware word representations"
    - "modeling recurrence for transformer"
    - "phrase based attentions"

Connected through hyperedge HE-2 (induced by source paper "learning efficient lexically constrained neural machine translation with external memory"):
  with members:
    - "towards one shot learning for rare word translation with external experts"
    - "fast lexically constrained decoding with dynamic beam allocation for neural machine translation"
    - "a call for clarity in reporting bleu scores"
    - "neural machine translation with key value memory augmented attention"
    - "phrase based neural unsupervised machine translation"
    - "unsupervised machine translation using monolingual corpora only"
    - "attention is all you need"
    - "lexically constrained decoding for sequence generation using grid beam search"

Connected through hyperedge HE-3 (induced by source paper "neural phrase to phrase machine translation"):
  with members:
    - "phrase based attentions"
    - "modeling localness for self attention networks"
    - "neural machine translation decoding with terminology constraints"
    - "the best of both worlds combining recent advances in neural machine translation"
    - "translating phrases in neural machine translation"
    - "attention is all you need"
    - "convolutional sequence to sequence learning"
    - "multitask learning with ctc and segmental crf for speech recognition"

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)