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 for explainable deep learning"
  Abstract: Recently, deep learning has been advancing the state of the art in artificial intelligence to a new level, and humans rely on artificial intelligence techniques more than ever. However, even with such unprecedented advancements, the lack of explanation regarding the decisions made by deep learning models and absence of control over their internal processes act as major drawbacks in critical decision-making processes, such as precision medicine and law enforcement. In response, efforts are being made to make deep learning interpretable and controllable by humans. In this paper, we review visual analytics, information visualization, and machine learning perspectives relevant to this aim, and discuss potential challenges and future research directions.

Connected through hyperedge HE-1 (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"

Connected through hyperedge HE-2 (induced by source paper "improving transparency of deep neural inference process"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "visual analytics in deep learning an interrogative survey for the next frontiers"
    - "methods for interpreting and understanding deep neural networks"
    - "open problems in engineering and quality assurance of safety critical machine learning systems"
    - "explainable software analytics"
    - "network dissection quantifying interpretability of deep visual representations"
    - "explaining nonlinear classification decisions with deep taylor decomposition"
    - "a unified approach to interpreting model predictions"

Connected through hyperedge HE-3 (induced by source paper "visual understanding of multiple attributes learning model of x ray scattering images"):
  with members:
    - "object detection with deep learning a review"
    - "a causal framework for explaining the predictions of black box sequence to sequence models"
    - "towards better analysis of deep convolutional neural networks"
    - "x ray scattering image classification using deep learning"
    - "yolo9000 better faster stronger"
    - "why should i trust you explaining the predictions of any classifier"
    - "understanding neural networks through deep visualization"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (induced by source paper "evaluating the readability of force directed graph layouts a deep learning approach"):
  with members:
    - "aesthetic discrimination of graph layouts"
    - "visual analytics in deep learning an interrogative survey for the next frontiers"
    - "what would a graph look like in this layout a machine learning approach to large graph visualization"
    - "understanding hidden memories of recurrent neural networks"
    - "towards better analysis of deep convolutional neural networks"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-5 (induced by source paper "uniqueness of medical data mining how the new technologies and data they generate are transforming medicine"):
  with members:
    - "a survey on deep learning in medical image analysis"
    - "statistical comparison of classifiers through bayesian hierarchical modelling"
    - "deep neural networks are easily fooled high confidence predictions for unrecognizable images"
    - "openml networked science in machine learning"
    - "intriguing properties of neural networks"

Connected through hyperedge HE-6 (induced by source paper "basis to develop a platform for multiple scale complex systems modeling and visualization monet"):
  with members:
    - "quantifying structure differences in literature using symbolic diversity and entropy criteria"
    - "calculating entropy at different scales among diverse communication systems"
    - "complexity measurement of natural and artificial languages"
    - "music viewed by its entropy content a novel window for comparative analysis"

Connected through hyperedge HE-7 (induced by source paper "a system for real time interactive analysis of deep learning training"):
  with members:
    - "imagenet training in minutes"
    - "towards better analysis of machine learning models a visual analytics perspective"
    - "nsml a machine learning platform that enables you to focus on your models"

Connected through hyperedge HE-8 (induced by source paper "visual analytics in deep learning an interrogative survey for the next frontiers"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "beyond saliency understanding convolutional neural networks from saliency prediction on layer wise relevance propagation"
    - "the challenge of crafting intelligible intelligence"
    - "visualbackprop efficient visualization of cnns"
    - "methods for interpreting and understanding deep neural networks"
    - "do convolutional neural networks learn class hierarchy"
    - "shield fast practical defense and vaccination for deep learning using jpeg compression"
    - "rnnbow visualizing learning via backpropagation gradients in rnns"

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)