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: "innvestigate neural networks"
  Abstract: In recent years, deep neural networks have revolutionized many application domains of machine learning and are key components of many critical decision or predictive processes. Therefore, it is crucial that domain specialists can understand and analyze actions and pre- dictions, even of the most complex neural network architectures. Despite these arguments neural networks are often treated as black boxes. In the attempt to alleviate this short- coming many analysis methods were proposed, yet the lack of reference implementations often makes a systematic comparison between the methods a major effort. The presented library iNNvestigate addresses this by providing a common interface and out-of-the- box implementation for many analysis methods, including the reference implementation for PatternNet and PatternAttribution as well as for LRP-methods. To demonstrate the versatility of iNNvestigate, we provide an analysis of image classifications for variety of state-of-the-art neural network architectures.

Connected through hyperedge HE-1 (induced by source paper "explainer a visual analytics framework for interactive and explainable machine learning"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "speculative execution for guided visual analytics"
    - "gan lab understanding complex deep generative models using interactive visual experimentation"
    - "human centered tools for coping with imperfect algorithms during medical decision making"
    - "retainvis visual analytics with interpretable and interactive recurrent neural networks on electronic medical records"
    - "manifold a model agnostic framework for interpretation and diagnosis of machine learning models"
    - "rulematrix visualizing and understanding classifiers with rules"
    - "visual analytics in deep learning an interrogative survey for the next frontiers"

Connected through hyperedge HE-2 (induced by source paper "when explanations lie why many modified bp attributions fail"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "fooling lime and shap adversarial attacks on post hoc explanation methods"
    - "how to manipulate cnns to make them lie the gradcam case"
    - "why are saliency maps noisy cause of and solution to noisy saliency maps"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "a theoretical explanation for perplexing behaviors of backpropagation based visualizations"
    - "understanding individual decisions of cnns via contrastive backpropagation"
    - "sanity checks for saliency maps"

Connected through hyperedge HE-3 (induced by source paper "black box saliency map generation using bayesian optimisation"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "smoothgrad removing noise by adding noise"
    - "interpretable explanations of black boxes by meaningful perturbation"
    - "learning deep features for discriminative localization"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "striving for simplicity the all convolutional net"
    - "visualizing and understanding convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "interpreting machine learning malware detectors which leverage n gram analysis"):
  with members:
    - "microsoft malware classification challenge"
    - "local interpretable model agnostic explanations of bayesian predictive models via kullback leibler projections"
    - "counterfactual explanations without opening the black box automated decisions and the gdpr"
    - "learning important features through propagating activation differences"
    - "why should i trust you explaining the predictions of any classifier"

Connected through hyperedge HE-5 (induced by source paper "verifying deep learning based decisions for facial expression recognition"):
  with members:
    - "towards best practice in explaining neural network decisions with lrp"
    - "towards real time head pose estimation exploring parameter reduced residual networks on in the wild datasets"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-6 (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-7 (induced by source paper "software and application patterns for explanation methods"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "unmasking clever hans predictors and assessing what machines really learn"
    - "evaluating neural network explanation methods using hybrid documents and morphological agreement"
    - "backprop evolution"
    - "methods for interpreting and understanding deep neural networks"
    - "towards computational fluorescence microscopy machine learning based integrated prediction of morphological and molecular tumor profiles"
    - "smoothgrad removing noise by adding noise"
    - "explaining recurrent neural network predictions in sentiment analysis"

Connected through hyperedge HE-8 (induced by source paper "explaining convolutional neural networks using softmax gradient layer wise relevance propagation"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "visualizing deep networks by optimizing with integrated gradients"
    - "deep features analysis with attention networks"
    - "how do convolutional neural networks learn design"
    - "attention branch network learning of attention mechanism for visual explanation"
    - "understanding individual decisions of cnns via contrastive backpropagation"
    - "explaining nonlinear classification decisions with deep taylor decomposition"
    - "axiomatic attribution for deep 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)