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 explanation"
  Abstract: Attention mechanisms have seen wide adoption in neural NLP models. In addition to improving predictive performance, these are often touted as affording transparency: models equipped with attention provide a distribution over attended-to input units, and this is often presented (at least implicitly) as communicating the relative importance of inputs. However, it is unclear what relationship exists between attention weights and model outputs. In this work, we perform extensive experiments across a variety of NLP tasks that aim to assess the degree to which attention weights provide meaningful `explanations' for predictions. We find that they largely do not. For example, learned attention weights are frequently uncorrelated with gradient-based measures of feature importance, and one can identify very different attention distributions that nonetheless yield equivalent predictions. Our findings show that standard attention modules do not provide meaningful explanations and should not be treated as though they do. Code for all experiments is available at this https URL.

Connected through hyperedge HE-1 (induced by source paper "counterfactual samples synthesizing for robust visual question answering"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "towards causal vqa revealing and reducing spurious correlations by invariant and covariant semantic editing"
    - "don t take the premise for granted mitigating artifacts in natural language inference"
    - "don t take the easy way out ensemble based methods for avoiding known dataset biases"
    - "adversarial regularization for visual question answering strengths shortcomings and side effects"
    - "learning by abstraction the neural state machine"
    - "block bilinear superdiagonal fusion for visual question answering and visual relationship detection"
    - "simple but effective techniques to reduce biases"

Connected through hyperedge HE-2 (induced by source paper "explaining explanations axiomatic feature interactions for deep networks"):
  with members:
    - "proxy tasks and subjective measures can be misleading in evaluating explainable ai systems"
    - "segment integrated gradients better attributions through regions"
    - "on identifiability in transformers"
    - "distilbert a distilled version of bert smaller faster cheaper and lighter"
    - "learning pairwise interactions with bayesian neural networks"
    - "incorporating priors with feature attribution on text classification"
    - "learning explainable models using attribution priors"
    - "is attention interpretable"

Connected through hyperedge HE-3 (induced by source paper "understanding image captioning models beyond visualizing attention"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "attention is not not explanation"
    - "bertscore evaluating text generation with bert"
    - "rubi reducing unimodal biases in visual question answering"
    - "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-4 (induced by source paper "further boosting bert based models by duplicating existing layers some intriguing phenomena inside bert"):
  with members:
    - "are sixteen heads really better than one"
    - "reducing transformer depth on demand with structured dropout"
    - "bert rediscovers the classical nlp pipeline"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "designing and interpreting probes with control tasks"
    - "albert a lite bert for self supervised learning of language representations"
    - "huggingface s transformers state of the art natural language processing"
    - "a multiscale visualization of attention in the transformer model"

Connected through hyperedge HE-5 (induced by source paper "what went wrong and when instance wise feature importance for time series models"):
  with members:
    - "is attention interpretable"
    - "what clinicians want contextualizing explainable machine learning for clinical end use"
    - "tsviz demystification of deep learning models for time series analysis"
    - "explaining an increase in predicted risk for clinical alerts"
    - "an interpretable lstm neural network for autoregressive exogenous model"
    - "raim recurrent attentive and intensive model of multimodal patient monitoring data"
    - "explaining image classifiers by counterfactual generation"
    - "sanity checks for saliency maps"

Connected through hyperedge HE-6 (induced by source paper "multi dimensional explanation of ratings from reviews"):
  with members:
    - "is attention interpretable"
    - "methods for interpreting and understanding deep neural networks"
    - "deep contextualized word representations"
    - "deriving machine attention from human rationales"
    - "attention is all you need"
    - "from softmax to sparsemax a sparse model of attention and multi label classification"
    - "rationalizing neural predictions"
    - "the concrete distribution a continuous relaxation of discrete random variables"

Connected through hyperedge HE-7 (induced by source paper "ham hybrid associations model with pooling for sequential recommendation"):
  with members:
    - "is attention interpretable"
    - "hierarchical gating networks for sequential recommendation"
    - "recurrent neural networks with top k gains for session based recommendations"
    - "personalized top n sequential recommendation via convolutional sequence embedding"
    - "self attentive sequential recommendation"
    - "fusing similarity models with markov chains for sparse sequential recommendation"
    - "ups and downs modeling the visual evolution of fashion trends with one class collaborative filtering"
    - "session based recommendations with recurrent neural networks"

Connected through hyperedge HE-8 (induced by source paper "cause learning granger causality from event sequences using attribution methods"):
  with members:
    - "is attention interpretable"
    - "axiomatic attribution for deep networks"
    - "learning important features through propagating activation differences"
    - "towards better understanding of gradient based attribution methods for deep neural networks"
    - "the neural hawkes process a neurally self modulating multivariate point process"
    - "learning granger causality for hawkes processes"
    - "show attend and tell neural image caption generation with visual attention"
    - "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)