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: "explaining predictions of non linear classifiers in nlp"
  Abstract: Layer-wise relevance propagation (LRP) is a recently proposed technique for explaining predictions of complex non-linear classifiers in terms of input variables. In this paper, we apply LRP for the first time to natural language processing (NLP). More precisely, we use it to explain the predictions of a convolutional neural network (CNN) trained on a topic categorization task. Our analysis highlights which words are relevant for a specific prediction of the CNN. We compare our technique to standard sensitivity analysis, both qualitatively and quantitatively, using a "word deleting" perturbation experiment, a PCA analysis, and various visualizations. All experiments validate the suitability of LRP for explaining the CNN predictions, which is also in line with results reported in recent image classification studies.

Connected through hyperedge HE-1 (induced by source paper "evaluating recurrent neural network explanations"):
  with members:
    - "analyzing and interpreting neural networks for nlp a report on the first blackboxnlp workshop"
    - "the emergence of number and syntax units in lstm language models"
    - "unmasking clever hans predictors and assessing what machines really learn"
    - "hierarchical interpretations for neural network predictions"
    - "explaining character aware neural networks for word level prediction do they discover linguistic rules"
    - "understanding convolutional neural networks for text classification"
    - "methods for interpreting and understanding deep neural networks"
    - "rudder return decomposition for delayed rewards"

Connected through hyperedge HE-2 (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"
    - "interpreting recurrent and attention based neural models a case study on natural language inference"

Connected through hyperedge HE-3 (induced by source paper "is attention interpretable"):
  with members:
    - "attention is not explanation"
    - "interpreting recurrent and attention based neural models a case study on natural language inference"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "towards understanding neural machine translation with word importance"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "assessing the ability of self attention networks to learn word order"
    - "attention is not explanation"
    - "train sort explain learning to diagnose translation models"
    - "adversarial attacks on deep learning models in natural language processing a survey"
    - "achieving human parity on automatic chinese to english news translation"
    - "how important is a neuron"
    - "has machine translation achieved human parity a case for document level evaluation"

Connected through hyperedge HE-5 (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"
    - "towards explainable nlp a generative explanation framework for text classification"

Connected through hyperedge HE-6 (induced by source paper "train sort explain learning to diagnose translation models"):
  with members:
    - "achieving human parity on automatic chinese to english news translation"
    - "innvestigate neural networks"
    - "learning explanations from language data"
    - "methods for interpreting and understanding deep neural networks"
    - "learning word vectors for 157 languages"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "universal language model fine tuning for text classification"
    - "explaining recurrent neural network predictions in sentiment analysis"

Connected through hyperedge HE-7 (induced by source paper "automating the search for a patent s prior art with a full text similarity search"):
  with members:
    - "patent retrieval a literature review"
    - "interactive exploration and discovery of scientific publications with pubvis"
    - "what is relevant in a text document an interpretable machine learning approach"
    - "science concierge a fast content based recommendation system for scientific publications"
    - "textual spatial cosine similarity"
    - "distributed representations of sentences and documents"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-8 (induced by source paper "deep text classification can be fooled"):
  with members:
    - "detecting adversarial image examples in deep neural networks with adaptive noise reduction"
    - "black box generation of adversarial text sequences to evade deep learning classifiers"
    - "adversarial texts with gradient methods"
    - "adversarial examples for evaluating reading comprehension systems"
    - "practical black box attacks against machine learning"
    - "towards evaluating the robustness of neural networks"
    - "character level convolutional networks for text classification"
    - "distillation as a defense to adversarial perturbations against deep neural networks"

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)