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: "adding interpretable attention to neural translation models improves word alignment"
  Abstract: Multi-layer models with multiple attention heads per layer provide superior translation quality compared to simpler and shallower models, but determining what source context is most relevant to each target word is more challenging as a result. Therefore, deriving high-accuracy word alignments from the activations of a state-of-the-art neural machine translation model is an open challenge. We propose a simple model extension to the Transformer architecture that makes use of its hidden representations and is restricted to attend solely on encoder information to predict the next word. It can be trained on bilingual data without word-alignment information. We further introduce a novel alignment inference procedure which applies stochastic gradient descent to directly optimize the attention activations towards a given target word. The resulting alignments dramatically outperform the naive approach to interpreting Transformer attention activations, and are comparable to Giza++ on two publicly available data sets.

Connected through hyperedge HE-1 (induced by source paper "on identifiability in transformers"):
  with members:
    - "attention is not not explanation"
    - "are sixteen heads really better than one"
    - "analyzing multi head self attention specialized heads do the heavy lifting the rest can be pruned"
    - "attention is not all you need for commonsense reasoning"
    - "visualizing and measuring the geometry of bert"
    - "bert rediscovers the classical nlp pipeline"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "is attention interpretable"

Connected through hyperedge HE-2 (induced by source paper "saliency driven word alignment interpretation for neural machine translation"):
  with members:
    - "on the alignment problem in multi head attention based neural machine translation"
    - "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 "understanding neural machine translation by simplification the case of encoder free models"):
  with members:
    - "on the evaluation of semantic phenomena in neural machine translation using natural language inference"
    - "evaluating layers of representation in neural machine translation on part of speech and semantic tagging tasks"
    - "a call for clarity in reporting bleu scores"
    - "an analysis of attention mechanisms the case of word sense disambiguation in neural machine translation"
    - "sockeye a toolkit for neural machine translation"
    - "attention is all you need"
    - "what does attention in neural machine translation pay attention to"
    - "attention focusing for neural machine translation by bridging source and target embeddings"

Connected through hyperedge HE-4 (induced by source paper "a discriminative neural model for cross lingual word alignment"):
  with members:
    - "on the alignment problem in multi head attention based neural machine translation"
    - "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:
    - "on the alignment problem in multi head attention based neural machine translation"
    - "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 "empirical evaluation of sequence to sequence models for word discovery in low resource settings"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "analyzing multi head self attention specialized heads do the heavy lifting the rest can be pruned"
    - "augmenting librispeech with french translations a multimodal corpus for direct speech translation evaluation"
    - "pervasive attention 2d convolutional neural networks for sequence to sequence prediction"
    - "hybrid self attention network for machine translation"
    - "multi head attention with disagreement regularization"
    - "a case study on using speech to translation alignments for language documentation"
    - "attention is all you need"

Connected through hyperedge HE-7 (induced by source paper "novel applications of factored neural machine translation"):
  with members:
    - "returnn as a generic flexible neural toolkit with application to translation and speech recognition"
    - "an operation sequence model for explainable neural machine translation"
    - "attention is all you need"
    - "bag of tricks for efficient text classification"
    - "incorporating discrete translation lexicons into neural machine translation"
    - "neural machine translation of rare words with subword units"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"

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)