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: "interpreting recurrent and attention based neural models a case study on natural language inference"
  Abstract: Deep learning models have achieved remarkable success in natural language inference (NLI) tasks. While these models are widely explored, they are hard to interpret and it is often unclear how and why they actually work. In this paper, we take a step toward explaining such deep learning based models through a case study on a popular neural model for NLI. In particular, we propose to interpret the intermediate layers of NLI models by visualizing the saliency of attention and LSTM gating signals. We present several examples for which our methods are able to reveal interesting insights and identify the critical information contributing to the model decisions.

Connected through hyperedge HE-1 (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-2 (induced by source paper "a neural approach to discourse relation signal detection"):
  with members:
    - "attention is not not explanation"
    - "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"
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"

Connected through hyperedge HE-3 (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"
    - "learning region features for object detection"
    - "deformable convnets v2 more deformable better results"

Connected through hyperedge HE-4 (induced by source paper "understanding multi head attention in abstractive summarization"):
  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"
    - "adaptively sparse transformers"
    - "analyzing the structure of attention in a transformer language model"
    - "is attention interpretable"
    - "attention is not explanation"
    - "attention interpretability across nlp tasks"
    - "do transformer attention heads provide transparency in abstractive summarization"

Connected through hyperedge HE-5 (induced by source paper "interrogating the explanatory power of attention in neural machine translation"):
  with members:
    - "saliency driven word alignment interpretation for neural machine translation"
    - "model explanations under calibration"
    - "an analysis of attention over clinical notes for predictive tasks"
    - "analyzing the structure of attention in a transformer language model"
    - "attention is not explanation"
    - "do transformer attention heads provide transparency in abstractive summarization"
    - "what does bert look at an analysis of bert s attention"
    - "explaining recurrent neural network predictions in sentiment analysis"

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"
    - "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-7 (induced by source paper "is attention interpretable"):
  with members:
    - "attention is not explanation"
    - "before name calling dynamics and triggers of ad hominem fallacies in web argumentation"
    - "sanity checks for saliency maps"
    - "learning structured text representations"
    - "satirical news detection and analysis using attention mechanism and linguistic features"
    - "attention is all you need"
    - "neural discourse structure for text categorization"
    - "spine sparse interpretable neural embeddings"

Connected through hyperedge HE-8 (induced by source paper "attention is not explanation"):
  with members:
    - "explainable prediction of medical codes from clinical text"
    - "deriving machine attention from human rationales"
    - "structured attention networks"
    - "a causal framework for explaining the predictions of black box sequence to sequence models"
    - "attention is all you need"
    - "axiomatic attribution for deep networks"
    - "an interpretable knowledge transfer model for knowledge base completion"
    - "right for the right reasons training differentiable models by constraining their explanations"

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)