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: "towards explainable nlp a generative explanation framework for text classification"
  Abstract: Building explainable systems is a critical problem in the field of Natural Language Processing (NLP), since most machine learning models provide no explanations for the predictions. Existing approaches for explainable machine learning systems tend to focus on interpreting the outputs or the connections between inputs and outputs. However, the fine-grained information is often ignored, and the systems do not explicitly generate the human-readable explanations. To better alleviate this problem, we propose a novel generative explanation framework that learns to make classification decisions and generate fine-grained explanations at the same time. More specifically, we introduce the explainable factor and the minimum risk training approach that learn to generate more reasonable explanations. We construct two new datasets that contain summaries, rating scores, and fine-grained reasons. We conduct experiments on both datasets, comparing with several strong neural network baseline systems. Experimental results show that our method surpasses all baselines on both datasets, and is able to generate concise explanations at the same time.

Connected through hyperedge HE-1 (induced by source paper "improving the interpretability of neural sentiment classifiers via data augmentation"):
  with members:
    - "eda easy data augmentation techniques for boosting performance on text classification tasks"
    - "attention is not explanation"
    - "generating token level explanations for natural language inference"
    - "what can ai do for me evaluating machine learning interpretations in cooperative play"
    - "contextual augmentation data augmentation by words with paradigmatic relations"
    - "transformation networks for target oriented sentiment classification"
    - "unsupervised discrete sentence representation learning for interpretable neural dialog generation"
    - "training augmentation with adversarial examples for robust speech recognition"

Connected through hyperedge HE-2 (induced by source paper "towards hierarchical importance attribution explaining compositional semantics for neural sequence models"):
  with members:
    - "bert rediscovers the classical nlp pipeline"
    - "roberta a robustly optimized bert pretraining approach"
    - "hierarchical interpretations for neural network predictions"
    - "evaluating neural network explanation methods using hybrid documents and morphological agreement"
    - "a survey of evaluation methods and measures for interpretable machine learning"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "explaining image classifiers by counterfactual generation"

Connected through hyperedge HE-3 (induced by source paper "human grounded evaluations of explanation methods for text classification"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "integrating semantic knowledge to tackle zero shot text classification"
    - "looking deeper into deep learning model attribution based explanations of textcnn"
    - "evaluating neural network explanation methods using hybrid documents and morphological agreement"
    - "understanding convolutional neural networks for text classification"
    - "interpreting recurrent and attention based neural models a case study on natural language inference"
    - "innvestigate neural networks"
    - "a human grounded evaluation benchmark for local explanations of machine learning"

Connected through hyperedge HE-4 (induced by source paper "neural abstractive text summarization and fake news detection"):
  with members:
    - "attending to entities for better text understanding"
    - "abstractive text summarization by incorporating reader comments"
    - "end to end alzheimer s disease diagnosis and biomarker identification"
    - "abstractive and extractive text summarization using document context vector and recurrent neural networks"
    - "end to end parkinson disease diagnosis using brain mr images by 3d cnn"
    - "wikihow a large scale text summarization dataset"
    - "text summarization techniques a brief survey"
    - "attention is all you need"

Connected through hyperedge HE-5 (induced by source paper "a framework for explainable text classification in legal document review"):
  with members:
    - "understanding convolutional neural networks for text classification"
    - "explainable text classification in legal document review a case study of explainable predictive coding"
    - "empirical evaluations of active learning strategies in legal document review"
    - "towards interpretable deep neural networks by leveraging adversarial examples"
    - "empirical evaluations of preprocessing parameters impact on predictive coding s effectiveness"
    - "rationalizing neural predictions"
    - "why should i trust you explaining the predictions of any classifier"
    - "rationale augmented convolutional neural networks for text 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)