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: "towards better analysis of deep convolutional neural networks"
  Abstract: Deep convolutional neural networks (CNNs) have achieved breakthrough performance in many pattern recognition tasks such as image classification. However, the development of high-quality deep models typically relies on a substantial amount of trial-and-error, as there is still no clear understanding of when and why a deep model works. In this paper, we present a visual analytics approach for better understanding, diagnosing, and refining deep CNNs. We formulate a deep CNN as a directed acyclic graph. Based on this formulation, a hybrid visualization is developed to disclose the multiple facets of each neuron and the interactions between them. In particular, we introduce a hierarchical rectangle packing algorithm and a matrix reordering algorithm to show the derived features of a neuron cluster. We also propose a biclustering-based edge bundling method to reduce visual clutter caused by a large number of connections between neurons. We evaluated our method on a set of CNNs and the results are generally favorable.

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 "classifying the classifier dissecting the weight space of neural networks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "topology of learning in artificial neural networks"
    - "visual analytics in deep learning an interrogative survey for the next frontiers"
    - "taskonomy disentangling task transfer learning"
    - "darts differentiable architecture search"
    - "do convolutional neural networks learn class hierarchy"
    - "network dissection quantifying interpretability of deep visual representations"
    - "interpreting deep visual representations via network dissection"

Connected through hyperedge HE-3 (induced by source paper "hypoml visual analysis for hypothesis based evaluation of machine learning models"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "gan lab understanding complex deep generative models using interactive visual experimentation"
    - "manifold a model agnostic framework for interpretation and diagnosis of machine learning models"
    - "deeptracker visualizing the training process of convolutional neural networks"
    - "visual interpretability for deep learning a survey"
    - "achieving non discrimination in prediction"
    - "understanding hidden memories of recurrent neural networks"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"

Connected through hyperedge HE-4 (induced by source paper "the what if tool interactive probing of machine learning models"):
  with members:
    - "rulematrix visualizing and understanding classifiers with rules"
    - "visual analytics in deep learning an interrogative survey for the next frontiers"
    - "ai fairness 360 an extensible toolkit for detecting understanding and mitigating unwanted algorithmic bias"
    - "smoothgrad removing noise by adding noise"
    - "counterfactual explanations without opening the black box automated decisions and the gdpr"
    - "equality of opportunity in supervised learning"
    - "visual analysis of hidden state dynamics in recurrent neural networks"
    - "why should i trust you explaining the predictions of any classifier"

Connected through hyperedge HE-5 (induced by source paper "nnva neural network assisted visual analysis of yeast cell polarization simulation"):
  with members:
    - "gan lab understanding complex deep generative models using interactive visual experimentation"
    - "visual analytics in deep learning an interrogative survey for the next frontiers"
    - "methods for interpreting and understanding deep neural networks"
    - "do convolutional neural networks learn class hierarchy"
    - "understanding hidden memories of recurrent neural networks"
    - "synthesizing the preferred inputs for neurons in neural networks via deep generator networks"
    - "lstmvis a tool for visual analysis of hidden state dynamics in recurrent neural networks"

Connected through hyperedge HE-6 (induced by source paper "oodanalyzer interactive analysis of out of distribution samples"):
  with members:
    - "recent research advances on interactive machine learning"
    - "enhancing the reliability of out of distribution image detection in neural networks"
    - "on calibration of modern neural networks"
    - "identifying unknown unknowns in the open world representations and policies for guided exploration"
    - "a baseline for detecting misclassified and out of distribution examples in neural networks"
    - "imagenet large scale visual recognition challenge"
    - "decaf a deep convolutional activation feature for generic visual recognition"

Connected through hyperedge HE-7 (induced by source paper "explanation in human ai systems a literature meta review synopsis of key ideas and publications and bibliography for explainable ai"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "ted teaching ai to explain its decisions"
    - "explaining explanations an overview of interpretability of machine learning"
    - "deep learning a critical appraisal"
    - "how do humans understand explanations from machine learning systems an evaluation of the human interpretability of explanation"
    - "explainable artificial intelligence xai the goodness criteria and the grasp ability test"
    - "interpretable explorable approximations of black box models"
    - "explaining the unexplained a class enhanced attentive response clear approach to understanding deep neural networks"

Connected through hyperedge HE-8 (induced by source paper "a multidisciplinary survey and framework for design and evaluation of explainable ai systems"):
  with members:
    - "fairsight visual analytics for fairness in decision making"
    - "predicting model failure using saliency maps in autonomous driving systems"
    - "human grounded evaluations of explanation methods for text classification"
    - "quantifying interpretability and trust in machine learning systems"
    - "summit scaling deep learning interpretability by visualizing activation and attribution summarizations"
    - "explainable artificial intelligence xai concepts taxonomies opportunities and challenges toward responsible ai"
    - "open issues in combating fake news interpretability as an opportunity"
    - "a survey on bias and fairness in machine learning"

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)