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: "visual analytics in deep learning an interrogative survey for the next frontiers"
  Abstract: Deep learning has recently seen rapid development and significant attention due to its state-of-the-art performance on previously-thought hard problems. However, because of the innate complexity and nonlinear structure of deep neural networks, the underlying decision making processes for why these models are achieving such high performance are challenging and sometimes mystifying to interpret. As deep learning spreads across domains, it is of paramount importance that we equip users of deep learning with tools for understanding when a model works correctly, when it fails, and ultimately how to improve its performance. Standardized toolkits for building neural networks have helped democratize deep learning; visual analytics systems have now been developed to support model explanation, interpretation, debugging, and improvement. We present a survey of the role of visual analytics in deep learning research, noting its short yet impactful history and summarize the state-of-the-art using a human-centered interrogative framework, focusing on the Five W's and How (Why, Who, What, How, When, and Where), to thoroughly summarize deep learning visual analytics research. We conclude by highlighting research directions and open research problems. This survey helps new researchers and practitioners in both visual analytics and deep learning to quickly learn key aspects of this young and rapidly growing body of research, whose impact spans a diverse range of domains.

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"
    - "innvestigate neural networks"

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"
    - "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"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"

Connected through hyperedge HE-3 (induced by source paper "the what if tool interactive probing of machine learning models"):
  with members:
    - "rulematrix visualizing and understanding classifiers with rules"
    - "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"
    - "towards better analysis of deep convolutional neural networks"
    - "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-4 (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"
    - "methods for interpreting and understanding deep neural networks"
    - "do convolutional neural networks learn class hierarchy"
    - "understanding hidden memories of recurrent neural networks"
    - "towards better analysis of deep convolutional 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-5 (induced by source paper "cnn 101 interactive visual learning for convolutional neural networks"):
  with members:
    - "tensorflow js machine learning for the web and beyond"
    - "gan lab understanding complex deep generative models using interactive visual experimentation"
    - "direct manipulation visualization of deep networks"
    - "tensorflow a system for large scale machine learning"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-6 (induced by source paper "visual evaluation of generative adversarial networks for time series data"):
  with members:
    - "gan lab understanding complex deep generative models using interactive visual experimentation"
    - "mad gan multivariate anomaly detection for time series data with generative adversarial networks"
    - "seqgan sequence generative adversarial nets with policy gradient"
    - "improved techniques for training gans"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"

Connected through hyperedge HE-7 (induced by source paper "exploring visual patterns in projected human and machine decision making paths"):
  with members:
    - "confusionflow a model agnostic visualization for temporal analysis of classifier confusion"
    - "understanding deep features with computer generated imagery"
    - "decaf a deep convolutional activation feature for generic visual recognition"

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.HC(Human-Computer Interaction)