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: "rationale augmented convolutional neural networks for text classification"
  Abstract: We present a new Convolutional Neural Network (CNN) model for text classification that jointly exploits labels on documents and their component sentences. Specifically, we consider scenarios in which annotators explicitly mark sentences (or snippets) that support their overall document categorization, i.e., they provide rationales. Our model exploits such supervision via a hierarchical approach in which each document is represented by a linear combination of the vector representations of its component sentences. We propose a sentence-level convolutional model that estimates the probability that a given sentence is a rationale, and we then scale the contribution of each sentence to the aggregate document representation in proportion to these estimates. Experiments on five classification datasets that have document labels and associated rationales demonstrate that our approach consistently outperforms strong baselines. Moreover, our model naturally provides explanations for its predictions.

Connected through hyperedge HE-1 (induced by source paper "eraser a benchmark to evaluate rationalized nlp models"):
  with members:
    - "boolq exploring the surprising difficulty of natural yes no questions"
    - "attention is not not explanation"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "learning credible deep neural networks with rationale regularization"):
  with members:
    - "on attribution of recurrent neural network predictions via additive decomposition"
    - "evaluating explanation without ground truth in interpretable machine learning"
    - "towards explanation of dnn based prediction with guided feature inversion"
    - "investigating backtranslation in neural machine translation"
    - "women also snowboard overcoming bias in captioning models"
    - "annotation artifacts in natural language inference data"
    - "knowledgeable reader enhancing cloze style reading comprehension with external commonsense knowledge"
    - "techniques for interpretable machine learning"

Connected through hyperedge HE-3 (induced by source paper "attention is not explanation"):
  with members:
    - "explainable prediction of medical codes from clinical text"
    - "interpreting recurrent and attention based neural models a case study on natural language inference"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "interpretable neural predictions with differentiable binary variables"):
  with members:
    - "backpropagating through structured argmax using a spigot"
    - "towards dynamic computation graphs via sparse latent structure"
    - "deep contextualized word representations"
    - "explaining explanations an overview of interpretability of machine learning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "universal language model fine tuning for text classification"
    - "a causal framework for explaining the predictions of black box sequence to sequence models"
    - "from softmax to sparsemax a sparse model of attention and multi label classification"

Connected through hyperedge HE-5 (induced by source paper "do human rationales improve machine explanations"):
  with members:
    - "attention is not explanation"
    - "grounding visual explanations"
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "deriving machine attention from human rationales"
    - "attention is all you need"
    - "a structured self attentive sentence embedding"
    - "rationalizing neural predictions"

Connected through hyperedge HE-6 (induced by source paper "weakly supervised domain detection"):
  with members:
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "attention is all you need"
    - "multiple instance learning networks for fine grained sentiment analysis"
    - "variations of the similarity function of textrank for automated summarization"
    - "very deep convolutional networks for text classification"
    - "domain separation networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "exploring hierarchical interaction between review and summary for better sentiment analysis"):
  with members:
    - "attention is not explanation"
    - "interpretable neural predictions with differentiable binary variables"
    - "a hierarchical end to end model for jointly improving text summarization and sentiment classification"
    - "attention is all you need"
    - "get to the point summarization with pointer generator networks"
    - "a thorough examination of the cnn daily mail reading comprehension task"
    - "ups and downs modeling the visual evolution of fashion trends with one class collaborative filtering"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "using clinical notes with time series data for icu management"):
  with members:
    - "clinicalbert modeling clinical notes and predicting hospital readmission"
    - "explainable prediction of medical codes from clinical text"
    - "raim recurrent attentive and intensive model of multimodal patient monitoring data"
    - "learning tasks for multitask learning heterogenous patient populations in the icu"
    - "improving hospital mortality prediction with medical named entities and multimodal learning"
    - "biosentvec creating sentence embeddings for biomedical texts"
    - "explain images with multimodal recurrent neural networks"
    - "convolutional neural networks for sentence classification"

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)