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: "analyzing classifiers fisher vectors and deep neural networks"
  Abstract: Fisher Vector classifiers and Deep Neural Networks (DNNs) are popular and successful algorithms for solving image classification problems. However, both are generally considered `black box' predictors as the non-linear transformations involved have so far prevented transparent and interpretable reasoning. Recently, a principled technique, Layer-wise Relevance Propagation (LRP), has been developed in order to better comprehend the inherent structured reasoning of complex nonlinear classification models such as Bag of Feature models or DNNs. In this paper we (1) extend the LRP framework also for Fisher Vector classifiers and then use it as analysis tool to (2) quantify the importance of context for classification, (3) qualitatively compare DNNs against FV classifiers in terms of important image regions and (4) detect potential flaws and biases in data. All experiments are performed on the PASCAL VOC 2007 data set.

Connected through hyperedge HE-1 (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-2 (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"
    - "innvestigate neural networks"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "unmasking clever hans predictors and assessing what machines really learn"):
  with members:
    - "deep neural networks for no reference and full reference image quality assessment"
    - "methods for interpreting and understanding deep neural networks"
    - "explaining the unique nature of individual gait patterns with deep learning"
    - "explaining recurrent neural network predictions in sentiment analysis"
    - "explaining nonlinear classification decisions with deep taylor decomposition"
    - "visualizing and understanding atari agents"
    - "what is relevant in a text document an interpretable machine learning approach"
    - "socially aware motion planning with deep reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "uncovering convolutional neural network decisions for diagnosing multiple sclerosis on conventional mri using layer wise relevance propagation"):
  with members:
    - "unmasking clever hans predictors and assessing what machines really learn"
    - "visualizing convolutional networks for mri based diagnosis of alzheimer s disease"
    - "interpretable lstms for whole brain neuroimaging analyses"
    - "innvestigate neural networks"
    - "methods for interpreting and understanding deep neural networks"
    - "convolutional neural networks for medical image analysis full training or fine tuning"
    - "chexnet radiologist level pneumonia detection on chest x rays with deep learning"
    - "axiomatic attribution for deep networks"

Connected through hyperedge HE-5 (induced by source paper "an overview of computational approaches for analyzing interpretation"):
  with members:
    - "methods for interpreting and understanding deep neural networks"
    - "multimodal social media analysis for gang violence prevention"
    - "adversarial defense based on structure to signal autoencoders"
    - "what do deep networks like to see"
    - "toward controlled generation of text"
    - "network dissection quantifying interpretability of deep visual representations"
    - "explaining recurrent neural network predictions in sentiment analysis"
    - "text summarization techniques a brief survey"

Connected through hyperedge HE-6 (induced by source paper "methods for interpreting and understanding deep neural networks"):
  with members:
    - "network dissection quantifying interpretability of deep visual representations"
    - "self normalizing neural networks"
    - "explaining recurrent neural network predictions in sentiment analysis"
    - "explaining nonlinear classification decisions with deep taylor decomposition"
    - "explaining how a deep neural network trained with end to end learning steers a car"
    - "the shattered gradients problem if resnets are the answer then what is the question"
    - "what is relevant in a text document an interpretable machine learning approach"
    - "synthesizing the preferred inputs for neurons in neural networks via deep generator networks"

Connected through hyperedge HE-7 (induced by source paper "explaining the unique nature of individual gait patterns with deep learning"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "methods for interpreting and understanding deep neural networks"
    - "explaining nonlinear classification decisions with deep taylor decomposition"
    - "what is relevant in a text document an interpretable machine learning approach"
    - "interpretable explanations of black boxes by meaningful perturbation"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "why should i trust you explaining the predictions of any classifier"
    - "top down neural attention by excitation backprop"

Connected through hyperedge HE-8 (induced by source paper "layer wise relevance propagation for explainable recommendations"):
  with members:
    - "deep learning based large scale visual recommendation and search for e commerce"
    - "explaining recurrent neural network predictions in sentiment analysis"
    - "explainable artificial intelligence understanding visualizing and interpreting deep learning models"
    - "why should i trust you explaining the predictions of any classifier"
    - "tensorflow a system for large scale machine learning"
    - "image based recommendations on styles and substitutes"
    - "adam a method for stochastic optimization"

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)