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: "on the alignment problem in multi head attention based neural machine translation"
  Abstract: This work investigates the alignment problem in state-of-the-art multi-head attention models based on the transformer architecture. We demonstrate that alignment extraction in transformer models can be improved by augmenting an additional alignment head to the multi-head source-to-target attention component. This is used to compute sharper attention weights. We describe how to use the alignment head to achieve competitive performance. To study the effect of adding the alignment head, we simulate a dictionary-guided translation task, where the user wants to guide translation using pre-defined dictionary entries. Using the proposed approach, we achieve up to $3.8$ % BLEU improvement when using the dictionary, in comparison to $2.4$ % BLEU in the baseline case. We also propose alignment pruning to speed up decoding in alignment-based neural machine translation (ANMT), which speeds up translation by a factor of $1.8$ without loss in translation performance. We carry out experiments on the shared WMT 2016 English$\to$Romanian news task and the BOLT Chinese$\to$English discussion forum task.

Connected through hyperedge HE-1 (induced by source paper "controlling utterance length in nmt based word segmentation with attention"):
  with members:
    - "attention is not not explanation"
    - "attention is not explanation"
    - "tied multitask learning for neural speech translation"
    - "learning to segment inputs for nmt favors character level processing"
    - "low resource speech to text translation"
    - "pre training on high resource speech recognition improves low resource speech to text translation"
    - "subword regularization improving neural network translation models with multiple subword candidates"
    - "learning when to concentrate or divert attention self adaptive attention temperature for neural machine translation"

Connected through hyperedge HE-2 (induced by source paper "saliency driven word alignment interpretation for neural machine translation"):
  with members:
    - "adding interpretable attention to neural translation models improves word alignment"
    - "interpreting recurrent and attention based neural models a case study on natural language inference"
    - "methods for interpreting and understanding deep neural networks"
    - "neural machine translation decoding with terminology constraints"
    - "an analysis of attention mechanisms the case of word sense disambiguation in neural machine translation"
    - "smoothgrad removing noise by adding noise"
    - "attention is all you need"
    - "what does attention in neural machine translation pay attention to"

Connected through hyperedge HE-3 (induced by source paper "code switching for enhancing nmt with pre specified translation"):
  with members:
    - "fast lexically constrained decoding with dynamic beam allocation for neural machine translation"
    - "neural machine translation decoding with terminology constraints"
    - "attention is all you need"
    - "lexically constrained decoding for sequence generation using grid beam search"
    - "get to the point summarization with pointer generator networks"
    - "searching for activation functions"
    - "pointing the unknown words"
    - "neural machine translation advised by statistical machine translation"

Connected through hyperedge HE-4 (induced by source paper "a discriminative neural model for cross lingual word alignment"):
  with members:
    - "adding interpretable attention to neural translation models improves word alignment"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "cross lingual argumentation mining machine translation and a bit of projection is all you need"
    - "sockeye a toolkit for neural machine translation"
    - "attention is all you need"
    - "what does attention in neural machine translation pay attention to"
    - "convolutional sequence to sequence learning"
    - "six challenges for neural machine translation"

Connected through hyperedge HE-5 (induced by source paper "jointly learning to align and translate with transformer models"):
  with members:
    - "adding interpretable attention to neural translation models improves word alignment"
    - "a call for clarity in reporting bleu scores"
    - "linguistically informed self attention for semantic role labeling"
    - "an analysis of attention mechanisms the case of word sense disambiguation in neural machine translation"
    - "understanding back translation at scale"
    - "attention is all you need"
    - "what does attention in neural machine translation pay attention to"
    - "decoupled weight decay regularization"

Connected through hyperedge HE-6 (induced by source paper "language model bootstrapping using neural machine translation for conversational speech recognition"):
  with members:
    - "a survey of domain adaptation for neural machine translation"
    - "massively multilingual sentence embeddings for zero shot cross lingual transfer and beyond"
    - "selecting machine translated data for quick bootstrapping of a natural language understanding system"
    - "attention is all you need"
    - "enriching word vectors with subword information"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "improving neural machine translation models with monolingual data"
    - "on using monolingual corpora in neural machine translation"

Connected through hyperedge HE-7 (induced by source paper "adding interpretable attention to neural translation models improves word alignment"):
  with members:
    - "attention is all you need"
    - "improving lexical choice in neural machine translation"
    - "incorporating discrete translation lexicons into neural machine translation"
    - "neural machine translation of rare words with subword units"
    - "neural machine translation by jointly learning to align and translate"
    - "sequence to sequence learning with neural networks"

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)