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: "evaluating recurrent neural network explanations"
  Abstract: Recently, several methods have been proposed to explain the predictions of recurrent neural networks (RNNs), in particular of LSTMs. The goal of these methods is to understand the network's decisions by assigning to each input variable, e.g., a word, a relevance indicating to which extent it contributed to a particular prediction. In previous works, some of these methods were not yet compared to one another, or were evaluated only qualitatively. We close this gap by systematically and quantitatively comparing these methods in different settings, namely (1) a toy arithmetic task which we use as a sanity check, (2) a five-class sentiment prediction of movie reviews, and besides (3) we explore the usefulness of word relevances to build sentence-level representations. Lastly, using the method that performed best in our experiments, we show how specific linguistic phenomena such as the negation in sentiment analysis reflect in terms of relevance patterns, and how the relevance visualization can help to understand the misclassification of individual samples.

Connected through hyperedge HE-1 (induced by source paper "understanding image captioning models beyond visualizing attention"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "attention is not not explanation"
    - "bertscore evaluating text generation with bert"
    - "rubi reducing unimodal biases in visual question answering"
    - "attention is not explanation"
    - "unmasking clever hans predictors and assessing what machines really learn"
    - "sanity checks for saliency maps"
    - "beyond word importance contextual decomposition to extract interactions from lstms"

Connected through hyperedge HE-2 (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-3 (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"
    - "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"
    - "beyond word importance contextual decomposition to extract interactions from lstms"

Connected through hyperedge HE-4 (induced by source paper "towards a unified evaluation of explanation methods without ground truth"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "evaluating explainers via perturbation"
    - "can i trust the explainer verifying post hoc explanatory methods"
    - "bim towards quantitative evaluation of interpretability methods with ground truth"
    - "evaluating explanation without ground truth in interpretable machine learning"
    - "net2vec quantifying and explaining how concepts are encoded by filters in deep neural networks"
    - "towards robust interpretability with self explaining neural networks"
    - "l shapley and c shapley efficient model interpretation for structured data"

Connected through hyperedge HE-5 (induced by source paper "tx ray quantifying and explaining model knowledge transfer in un supervised nlp"):
  with members:
    - "episodic memory in lifelong language learning"
    - "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"
    - "are sixteen heads really better than one"
    - "analyzing multi head self attention specialized heads do the heavy lifting the rest can be pruned"
    - "continual lifelong learning with neural networks a review"
    - "to tune or not to tune adapting pretrained representations to diverse tasks"
    - "visual interaction with deep learning models through collaborative semantic inference"
    - "universal adversarial triggers for attacking and analyzing nlp"

Connected through hyperedge HE-6 (induced by source paper "towards a rigorous evaluation of xai methods on time series"):
  with members:
    - "explaining deep classification of time series data with learned prototypes"
    - "a survey of evaluation methods and measures for interpretable machine learning"
    - "yolov3 an incremental improvement"
    - "explainable artificial intelligence understanding visualizing and interpreting deep learning models"
    - "a unified approach to interpreting model predictions"
    - "learning important features through propagating activation differences"
    - "an empirical evaluation of deep learning on highway driving"
    - "visualizing and understanding convolutional 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.LG(Machine Learning)