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 character aware neural networks for word level prediction do they discover linguistic rules"
  Abstract: Character-level features are currently used in different neural network-based natural language processing algorithms. However, little is known about the character-level patterns those models learn. Moreover, models are often compared only quantitatively while a qualitative analysis is missing. In this paper, we investigate which character-level patterns neural networks learn and if those patterns coincide with manually-defined word segmentations and annotations. To that end, we extend the contextual decomposition technique (Murdoch et al. 2018) to convolutional neural networks which allows us to compare convolutional neural networks and bidirectional long short-term memory networks. We evaluate and compare these models for the task of morphological tagging on three morphologically different languages and show that these models implicitly discover understandable linguistic rules. Our implementation can be found at this https URL .

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"
    - "understanding convolutional neural networks for text classification"
    - "methods for interpreting and understanding deep neural networks"
    - "rudder return decomposition for delayed rewards"
    - "an empirical analysis of the role of amplifiers downtoners and negations in emotion classification in microblogs"

Connected through hyperedge HE-2 (induced by source paper "tabula nearly rasa probing the linguistic knowledge of character level neural language models trained on unsegmented text"):
  with members:
    - "revisiting character based neural machine translation with capacity and compression"
    - "indicatements that character language models learn english morpho syntactic units and regularities"
    - "assessing composition in sentence vector representations"
    - "an analysis of neural language modeling at multiple scales"
    - "revisiting the poverty of the stimulus hierarchical generalization without a hierarchical bias in recurrent neural networks"
    - "recurrent neural networks in linguistic theory revisiting pinker and prince 1988 and the past tense debate"
    - "learning to generate reviews and discovering sentiment"
    - "are all languages equally hard to language model"

Connected through hyperedge HE-3 (induced by source paper "ls tree model interpretation when the data are linguistic"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "rationalizing neural predictions"
    - "why should i trust you explaining the predictions of any classifier"
    - "understanding neural networks through representation erasure"
    - "visualizing and understanding neural models in nlp"
    - "ask attend and answer exploring question guided spatial attention for visual question answering"
    - "stacked attention networks for image question answering"

Connected through hyperedge HE-4 (induced by source paper "hierarchical interpretations for neural network predictions"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "beyond word importance contextual decomposition to extract interactions from lstms"
    - "smoothgrad removing noise by adding noise"
    - "interpreting cnn knowledge via an explanatory graph"
    - "automatic rule extraction from long short term memory networks"
    - "foolbox v0 8 0 a python toolbox to benchmark the robustness of machine learning models"
    - "axiomatic attribution for deep networks"
    - "a unified approach to interpreting model predictions"

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)