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: "what is relevant in a text document an interpretable machine learning approach"
  Abstract: Text documents can be described by a number of abstract concepts such as semantic category, writing style, or sentiment. Machine learning (ML) models have been trained to automatically map documents to these abstract concepts, allowing to annotate very large text collections, more than could be processed by a human in a lifetime. Besides predicting the text’s category very accurately, it is also highly desirable to understand how and why the categorization process takes place. In this paper, we demonstrate that such understanding can be achieved by tracing the classification decision back to individual words using layer-wise relevance propagation (LRP), a recently developed technique for explaining predictions of complex non-linear classifiers. We train two word-based ML models, a convolutional neural network (CNN) and a bag-of-words SVM classifier, on a topic categorization task and adapt the LRP method to decompose the predictions of these models onto words. Resulting scores indicate how much individual words contribute to the overall classification decision. This enables one to distill relevant information from text documents without an explicit semantic information extraction step. We further use the word-wise relevance scores for generating novel vector-based document representations which capture semantic information. Based on these document vectors, we introduce a measure of model explanatory power and show that, although the SVM and CNN models perform similarly in terms of classification accuracy, the latter exhibits a higher level of explainability which makes it more comprehensible for humans and potentially more useful for other applications.

Connected through hyperedge HE-1 (induced by source paper "towards crisp ml q a machine learning process model with quality assurance methodology"):
  with members:
    - "quantifying interpretability and trust in machine learning systems"
    - "machine learning testing survey landscapes and horizons"
    - "deep visual domain adaptation a survey"
    - "deep reinforcement learning"
    - "on the state of the art of evaluation in neural language models"
    - "machine teaching a new paradigm for building machine learning systems"
    - "deeptest automated testing of deep neural network driven autonomous cars"
    - "why should i trust you explaining the predictions of any classifier"

Connected through hyperedge HE-2 (induced by source paper "metafeatures based rule extraction for classifiers on behavioral and textual data"):
  with members:
    - "the value of big data for credit scoring enhancing financial inclusion using mobile phone data and social network analytics"
    - "rule induction for global explanation of trained models"
    - "why should i trust you explaining the predictions of any classifier"
    - "deep inside convolutional networks visualising image classification models and saliency maps"
    - "recommender system for online dating service"
    - "technical note bias and the quantification of stability"

Connected through hyperedge HE-3 (induced by source paper "a survey on explainable artificial intelligence xai towards medical xai"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "interpretable neural networks for predicting mortality risk using multi modal electronic health records"
    - "an evaluation of the human interpretability of explanation"
    - "protoattend attention based prototypical learning"
    - "understanding the un interpretability of natural image distributions using generative models"
    - "outlining the design space of explainable intelligent systems for medical diagnosis"
    - "chexpert a large chest radiograph dataset with uncertainty labels and expert comparison"
    - "interpretable dynamics models for data efficient reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "towards explainable artificial intelligence"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "an evaluation of the human interpretability of explanation"
    - "transparency motivations and challenges"
    - "interpretable deep learning in drug discovery"
    - "from clustering to cluster explanations via neural networks"
    - "uncovering convolutional neural network decisions for diagnosing multiple sclerosis on conventional mri using layer wise relevance propagation"
    - "unmasking clever hans predictors and assessing what machines really learn"
    - "understanding neural networks via feature visualization a survey"

Connected through hyperedge HE-5 (induced by source paper "quick and not so dirty unsupervised selection of justification sentences for multi hop question answering"):
  with members:
    - "improving question answering with external knowledge"
    - "understanding dataset design choices for multi hop reasoning"
    - "evidence sentence extraction for machine reading comprehension"
    - "question answering as global reasoning over semantic abstractions"
    - "dream a challenge dataset and models for dialogue based reading comprehension"
    - "repurposing entailment for multi hop question answering tasks"
    - "an interpretable reasoning network for multi relation question answering"
    - "can a suit of armor conduct electricity a new dataset for open book question answering"

Connected through hyperedge HE-6 (induced by source paper "explaining and interpreting lstms"):
  with members:
    - "from clustering to cluster explanations via neural networks"
    - "unmasking clever hans predictors and assessing what machines really learn"
    - "evaluating recurrent neural network explanations"
    - "methods for interpreting and understanding deep neural networks"
    - "interpreting and explaining deep neural networks for classification of audio signals"
    - "rudder return decomposition for delayed rewards"
    - "learning to explain an information theoretic perspective on model interpretation"
    - "explaining the unique nature of individual gait patterns with deep learning"

Connected through hyperedge HE-7 (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-8 (induced by source paper "unmasking clever hans predictors and assessing what machines really learn"):
  with members:
    - "deep neural networks for no reference and full reference image quality assessment"
    - "methods for interpreting and understanding deep neural networks"
    - "explaining the unique nature of individual gait patterns with deep learning"
    - "explaining recurrent neural network predictions in sentiment analysis"
    - "explaining nonlinear classification decisions with deep taylor decomposition"
    - "visualizing and understanding atari agents"
    - "socially aware motion planning with deep reinforcement learning"
    - "deepstack expert level artificial intelligence in heads up no limit poker"

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)