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: "attention is not not explanation"
  Abstract: Attention mechanisms play a central role in NLP systems, especially within recurrent neural network (RNN) models. Recently, there has been increasing interest in whether or not the intermediate representations offered by these modules may be used to explain the reasoning for a model's prediction, and consequently reach insights regarding the model's decision-making process. A recent paper claims that `Attention is not Explanation' (Jain and Wallace, 2019). We challenge many of the assumptions underlying this work, arguing that such a claim depends on one's definition of explanation, and that testing it needs to take into account all elements of the model, using a rigorous experimental design. We propose four alternative tests to determine when/whether attention can be used as explanation: a simple uniform-weights baseline; a variance calibration based on multiple random seed runs; a diagnostic framework using frozen weights from pretrained models; and an end-to-end adversarial attention training protocol. Each allows for meaningful interpretation of attention mechanisms in RNN models. We show that even when reliable adversarial distributions can be found, they don't perform well on the simple diagnostic, indicating that prior work does not disprove the usefulness of attention mechanisms for explainability.

Connected through hyperedge HE-1 (induced by source paper "understanding image captioning models beyond visualizing attention"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "bertscore evaluating text generation with bert"
    - "rubi reducing unimodal biases in visual question answering"
    - "attention is not explanation"
    - "unmasking clever hans predictors and assessing what machines really learn"
    - "evaluating recurrent neural network explanations"
    - "sanity checks for saliency maps"
    - "beyond word importance contextual decomposition to extract interactions from lstms"

Connected through hyperedge HE-2 (induced by source paper "recast interactive auditing of automatic toxicity detection models"):
  with members:
    - "what bert is not lessons from a new suite of psycholinguistic diagnostics for language models"
    - "distilbert a distilled version of bert smaller faster cheaper and lighter"
    - "sanvis visual analytics for understanding self attention networks"
    - "black is to criminal as caucasian is to police detecting and removing multiclass bias in word embeddings"
    - "how can we fool lime and shap adversarial attacks on post hoc explanation methods"
    - "huggingface s transformers state of the art natural language processing"
    - "exbert a visual analysis tool to explore learned representations in transformers models"
    - "gender bias in contextualized word embeddings"

Connected through hyperedge HE-3 (induced by source paper "a neural approach to discourse relation signal detection"):
  with members:
    - "gumdrop at the disrpt2019 shared task a model stacking approach to discourse unit segmentation and connective detection"
    - "predicting discourse structure using distant supervision from sentiment"
    - "beyond the wall street journal anchoring and comparing discourse signals across genres"
    - "interpreting recurrent and attention based neural models a case study on natural language inference"
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"

Connected through hyperedge HE-4 (induced by source paper "unsupervised extraction of market moving events with neural attention"):
  with members:
    - "is attention interpretable"
    - "attention is not explanation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "leverage financial news to predict stock price movements using word embeddings and deep neural networks"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-5 (induced by source paper "on identifiability in transformers"):
  with members:
    - "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"
    - "is attention interpretable"

Connected through hyperedge HE-6 (induced by source paper "eraser a benchmark to evaluate rationalized nlp models"):
  with members:
    - "boolq exploring the surprising difficulty of natural yes no questions"
    - "on identifiability in transformers"
    - "inferring which medical treatments work from reports of clinical trials"
    - "fine grained sentiment analysis with faithful attention"
    - "on the validity of self attention as explanation in transformer models"
    - "rethinking cooperative rationalization introspective extraction and complement control"
    - "is attention interpretable"
    - "seeing things from a different angle discovering diverse perspectives about claims"

Connected through hyperedge HE-7 (induced by source paper "controlling utterance length in nmt based word segmentation with attention"):
  with members:
    - "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"
    - "on the alignment problem in multi head attention based neural machine 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-8 (induced by source paper "attention interpretability across nlp tasks"):
  with members:
    - "bert rediscovers the classical nlp pipeline"
    - "analyzing the structure of attention in a transformer language model"
    - "is attention interpretable"
    - "attention is not explanation"
    - "what does bert look at an analysis of bert s attention"
    - "interpreting recurrent and attention based neural models a case study on natural language inference"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "learning structured text representations"

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)