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: "extraction of salient sentences from labelled documents"
  Abstract: We present a hierarchical convolutional document model with an architecture designed to support introspection of the document structure. Using this model, we show how to use visualisation techniques from the computer vision literature to identify and extract topic-relevant sentences. We also introduce a new scalable evaluation technique for automatic sentence extraction systems that avoids the need for time consuming human annotation of validation data.

Connected through hyperedge HE-1 (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-2 (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-3 (induced by source paper "on attribution of recurrent neural network predictions via additive decomposition"):
  with members:
    - "towards explanation of dnn based prediction with guided feature inversion"
    - "evaluating neural network explanation methods using hybrid documents and morphological prediction"
    - "techniques for interpretable machine learning"
    - "exact and consistent interpretation for piecewise linear neural networks a closed form solution"
    - "deep contextualized word representations"
    - "fine tuned language models for text classification"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "interpretable adversarial perturbation in input embedding space for text"

Connected through hyperedge HE-4 (induced by source paper "weakly supervised attention networks for fine grained opinion mining and public health"):
  with members:
    - "attention based deep multiple instance learning"
    - "zero shot sequence labeling transferring knowledge from sentences to tokens"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "deriving machine attention from human rationales"
    - "structured attention networks"
    - "multiple instance learning a survey of problem characteristics and applications"
    - "multiple instance learning networks for fine grained sentiment analysis"
    - "revisiting recurrent networks for paraphrastic sentence embeddings"

Connected through hyperedge HE-5 (induced by source paper "automatic text summarization what has been done and what has to be done"):
  with members:
    - "toward abstractive summarization using semantic representations"
    - "learning based single document summarization with compression and anaphoricity constraints"
    - "a neural attention model for abstractive sentence summarization"
    - "on using very large target vocabulary for neural machine translation"
    - "bayesian query focused summarization"
    - "lexrank graph based lexical centrality as salience in text summarization"
    - "inferring strategies for sentence ordering in multidocument news summarization"
    - "applying natural language generation to indicative summarization"

Connected through hyperedge HE-6 (induced by source paper "what is relevant in a text document an interpretable machine learning approach"):
  with members:
    - "methods for interpreting and understanding deep neural networks"
    - "explaining recurrent neural network predictions in sentiment analysis"
    - "explaining nonlinear classification decisions with deep taylor decomposition"
    - "identifying individual facial expressions by deconstructing a neural network"
    - "explaining predictions of non linear classifiers in nlp"
    - "why should i trust you explaining the predictions of any classifier"
    - "character level convolutional networks for text classification"
    - "visualizing and understanding neural models in nlp"

Connected through hyperedge HE-7 (induced by source paper "a hierarchical model of reviews for aspect based sentiment analysis"):
  with members:
    - "harnessing deep neural networks with logic rules"
    - "insight 1 at semeval 2016 task 5 deep learning for multilingual aspect based sentiment analysis"
    - "a hierarchical latent variable encoder decoder model for generating dialogues"
    - "a hierarchical neural autoencoder for paragraphs and documents"
    - "effective lstms for target dependent sentiment classification"
    - "adam a method for stochastic optimization"
    - "sequence to sequence learning with neural networks"
    - "speech recognition with deep recurrent neural networks"

Connected through hyperedge HE-8 (induced by source paper "explaining predictions of non linear classifiers in nlp"):
  with members:
    - "explaining nonlinear classification decisions with deep taylor decomposition"
    - "visualizing and understanding neural models in nlp"
    - "evaluating the visualization of what a deep neural network has learned"
    - "convolutional neural networks for sentence classification"
    - "visualizing and understanding convolutional networks"
    - "deep inside convolutional networks visualising image classification models and saliency maps"
    - "efficient estimation of word representations in vector space"
    - "natural language processing almost from scratch"

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)