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: "an analysis of attention mechanisms the case of word sense disambiguation in neural machine translation"
  Abstract: Recent work has shown that the encoder-decoder attention mechanisms in neural machine translation (NMT) are different from the word alignment in statistical machine translation. In this paper, we focus on analyzing encoder-decoder attention mechanisms, in the case of word sense disambiguation (WSD) in NMT models. We hypothesize that attention mechanisms pay more attention to context tokens when translating ambiguous words. We explore the attention distribution patterns when translating ambiguous nouns. Counter-intuitively, we find that attention mechanisms are likely to distribute more attention to the ambiguous noun itself rather than context tokens, in comparison to other nouns. We conclude that attention mechanism is not the main mechanism used by NMT models to incorporate contextual information for WSD. The experimental results suggest that NMT models learn to encode contextual information necessary for WSD in the encoder hidden states. For the attention mechanism in Transformer models, we reveal that the first few layers gradually learn to "align" source and target tokens and the last few layers learn to extract features from the related but unaligned context tokens.

Connected through hyperedge HE-1 (induced by source paper "an empirical study of spatial attention mechanisms in deep networks"):
  with members:
    - "pay less attention with lightweight and dynamic convolutions"
    - "attention is not explanation"
    - "transformer xl attentive language models beyond a fixed length context"
    - "ocnet object context network for scene parsing"
    - "self attention with relative position representations"
    - "ccnet criss cross attention for semantic segmentation"
    - "interpreting recurrent and attention based neural models a case study on natural language inference"
    - "learning region features for object detection"

Connected through hyperedge HE-2 (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"
    - "adding interpretable attention to neural translation models improves word alignment"

Connected through hyperedge HE-3 (induced by source paper "what do you mean bert assessing bert as a distributional semantics model"):
  with members:
    - "distributional semantics and linguistic theory"
    - "are sixteen heads really better than one"
    - "on identifiability in transformers"
    - "analyzing multi head self attention specialized heads do the heavy lifting the rest can be pruned"
    - "visualizing and measuring the geometry of bert"
    - "spanbert improving pre training by representing and predicting spans"
    - "is attention interpretable"
    - "designing and interpreting probes with control tasks"

Connected through hyperedge HE-4 (induced by source paper "augmenting neural machine translation with knowledge graphs"):
  with members:
    - "leveraging knowledge bases in lstms for improving machine reading"
    - "learning beyond datasets knowledge graph augmented neural networks for natural language processing"
    - "modeling semantics with gated graph neural networks for knowledge base question answering"
    - "machine translation using semantic web technologies a survey"
    - "an evaluation of neural machine translation models on historical spelling normalization"
    - "open domain question answering using early fusion of knowledge bases and text"
    - "learning word vectors for 157 languages"
    - "the importance of being recurrent for modeling hierarchical structure"

Connected through hyperedge HE-5 (induced by source paper "hard but robust easy but sensitive how encoder and decoder perform in neural machine translation"):
  with members:
    - "mass masked sequence to sequence pre training for language generation"
    - "universal transformers"
    - "achieving human parity on automatic chinese to english news translation"
    - "hybrid self attention network for machine translation"
    - "beyond error propagation in neural machine translation characteristics of language also matter"
    - "non autoregressive neural machine translation with enhanced decoder input"
    - "evaluating layers of representation in neural machine translation on part of speech and semantic tagging tasks"
    - "sharp nearby fuzzy far away how neural language models use context"

Connected through hyperedge HE-6 (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"
    - "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"
    - "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-7 (induced by source paper "understanding neural machine translation by simplification the case of encoder free models"):
  with members:
    - "adding interpretable attention to neural translation models improves word alignment"
    - "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"
    - "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-8 (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"
    - "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"
    - "understanding back translation at scale"
    - "attention is all you need"
    - "what does attention in neural machine translation pay attention to"
    - "decoupled weight decay regularization"

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)