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 the visualization of what a deep neural network has learned"
  Abstract: Deep Neural Networks (DNNs) have demonstrated impressive performance in complex machine learning tasks such as image classification or speech recognition. However, due to their multi-layer nonlinear structure, they are not transparent, i.e., it is hard to grasp what makes them arrive at a particular classification or recognition decision given a new unseen data sample. Recently, several approaches have been proposed enabling one to understand and interpret the reasoning embodied in a DNN for a single test image. These methods quantify the ''importance'' of individual pixels wrt the classification decision and allow a visualization in terms of a heatmap in pixel/input space. While the usefulness of heatmaps can be judged subjectively by a human, an objective quality measure is missing. In this paper we present a general methodology based on region perturbation for evaluating ordered collections of pixels such as heatmaps. We compare heatmaps computed by three different methods on the SUN397, ILSVRC2012 and MIT Places data sets. Our main result is that the recently proposed Layer-wise Relevance Propagation (LRP) algorithm qualitatively and quantitatively provides a better explanation of what made a DNN arrive at a particular classification decision than the sensitivity-based approach or the deconvolution method. We provide theoretical arguments to explain this result and discuss its practical implications. Finally, we investigate the use of heatmaps for unsupervised assessment of neural network performance.

Connected through hyperedge HE-1 (induced by source paper "counterfactual explanation algorithms for behavioral and textual data"):
  with members:
    - "interpretable counterfactual explanations guided by prototypes"
    - "efficient search for diverse coherent explanations"
    - "grounding visual explanations"
    - "explanation methods in deep learning users values concerns and challenges"
    - "local rule based explanations of black box decision systems"
    - "explanation in artificial intelligence insights from the social sciences"
    - "counterfactual explanations without opening the black box automated decisions and the gdpr"
    - "a unified approach to interpreting model predictions"

Connected through hyperedge HE-2 (induced by source paper "lamvi 2 a visual tool for comparing and tuning word embedding models"):
  with members:
    - "visual analytics in deep learning an interrogative survey for the next frontiers"
    - "do convolutional neural networks learn class hierarchy"
    - "understanding hidden memories of recurrent neural networks"
    - "towards better analysis of machine learning models a visual analytics perspective"
    - "visual analysis of hidden state dynamics in recurrent neural networks"
    - "understanding neural networks through deep visualization"
    - "visualizing and understanding recurrent networks"
    - "line large scale information network embedding"

Connected through hyperedge HE-3 (induced by source paper "visualbackprop efficient visualization of cnns"):
  with members:
    - "visualizing deep neural network decisions prediction difference analysis"
    - "end to end learning for self driving cars"
    - "identity mappings in deep residual networks"
    - "understanding neural networks through deep visualization"
    - "deepdriving learning affordance for direct perception in autonomous driving"
    - "learning deep features for discriminative localization"
    - "understanding deep image representations by inverting them"
    - "striving for simplicity the all convolutional net"

Connected through hyperedge HE-4 (induced by source paper "how important is a neuron"):
  with members:
    - "understanding neural networks through deep visualization"
    - "inverting visual representations with convolutional networks"
    - "understanding deep image representations by inverting them"
    - "striving for simplicity the all convolutional net"
    - "deep inside convolutional networks visualising image classification models and saliency maps"
    - "building high level features using large scale unsupervised learning"

Connected through hyperedge HE-5 (induced by source paper "it s reducing a human being to a percentage perceptions of justice in algorithmic decisions"):
  with members:
    - "explaining nonlinear classification decisions with deep taylor decomposition"
    - "challenges for transparency"
    - "the mythos of model interpretability"
    - "why should i trust you explaining the predictions of any classifier"

Connected through hyperedge HE-6 (induced by source paper "axiomatic attribution for deep networks"):
  with members:
    - "learning important features through propagating activation differences"
    - "an unexpected unity among methods for interpreting model predictions"
    - "learning executable semantic parsers for natural language understanding"
    - "not just a black box learning important features through propagating activation differences"
    - "why should i trust you explaining the predictions of any classifier"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "understanding neural networks through deep visualization"
    - "inverting visual representations with convolutional networks"

Connected through hyperedge HE-7 (induced by source paper "understanding convolutional neural networks with a mathematical model"):
  with members:
    - "explaining nonlinear classification decisions with deep taylor decomposition"
    - "towards better analysis of deep convolutional neural networks"
    - "multifaceted feature visualization uncovering the different types of features learned by each neuron in deep neural networks"
    - "explaining predictions of non linear classifiers in nlp"
    - "a new method to visualize deep neural networks"
    - "understanding neural networks through deep visualization"
    - "inverting visual representations with convolutional networks"
    - "a mathematical theory of deep convolutional neural networks for feature extraction"

Connected through hyperedge HE-8 (induced by source paper "identifying individual facial expressions by deconstructing a neural network"):
  with members:
    - "explaining nonlinear classification decisions with deep taylor decomposition"
    - "multifaceted feature visualization uncovering the different types of features learned by each neuron in deep neural networks"
    - "explaining predictions of non linear classifiers in nlp"
    - "interpretable deep neural networks for single trial eeg classification"
    - "a new method to visualize deep neural networks"
    - "inverting visual representations with convolutional networks"
    - "deep residual learning for image recognition"
    - "analyzing classifiers fisher vectors and deep neural 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.CV(Computer Vision and Pattern Recognition)