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: "deeptracker visualizing the training process of convolutional neural networks"
  Abstract: Deep convolutional neural networks (CNNs) have achieved remarkable success in various fields. However, training an excellent CNN is practically a trial-and-error process that consumes a tremendous amount of time and computer resources. To accelerate the training process and reduce the number of trials, experts need to understand what has occurred in the training process and why the resulting CNN behaves as such. However, current popular training platforms, such as TensorFlow, only provide very little and general information, such as training/validation errors, which is far from enough to serve this purpose. To bridge this gap and help domain experts with their training tasks in a practical environment, we propose a visual analytics system, DeepTracker, to facilitate the exploration of the rich dynamics of CNN training processes and to identify the unusual patterns that are hidden behind the huge amount of training log. Specifically,we combine a hierarchical index mechanism and a set of hierarchical small multiples to help experts explore the entire training log from different levels of detail. We also introduce a novel cube-style visualization to reveal the complex correlations among multiple types of heterogeneous training data including neuron weights, validation images, and training iterations. Three case studies are conducted to demonstrate how DeepTracker provides its users with valuable knowledge in an industry-level CNN training process, namely in our case, training ResNet-50 on the ImageNet dataset. We show that our method can be easily applied to other state-of-the-art "very deep" CNN models.

Connected through hyperedge HE-1 (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"
    - "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"
    - "towards better analysis of deep convolutional neural networks"

Connected through hyperedge HE-2 (induced by source paper "sanvis visual analytics for understanding self attention networks"):
  with members:
    - "analyzing multi head self attention specialized heads do the heavy lifting the rest can be pruned"
    - "analyzing the structure of attention in a transformer language model"
    - "gan lab understanding complex deep generative models using interactive visual experimentation"
    - "retainvis visual analytics with interpretable and interactive recurrent neural networks on electronic medical records"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "what does bert look at an analysis of bert s attention"
    - "do convolutional neural networks learn class hierarchy"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-3 (induced by source paper "confusionflow a model agnostic visualization for temporal analysis of classifier confusion"):
  with members:
    - "gan lab understanding complex deep generative models using interactive visual experimentation"
    - "manifold a model agnostic framework for interpretation and diagnosis of machine learning models"
    - "visual analytics in deep learning an interrogative survey for the next frontiers"
    - "do cifar 10 classifiers generalize to cifar 10"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "do convolutional neural networks learn class hierarchy"
    - "rethinking the value of network pruning"
    - "cnncomparator comparative analytics of convolutional neural networks"

Connected through hyperedge HE-4 (induced by source paper "atmseer increasing transparency and controllability in automated machine learning"):
  with members:
    - "manifold a model agnostic framework for interpretation and diagnosis of machine learning models"
    - "cnncomparator comparative analytics of convolutional neural networks"
    - "large scale evolution of image classifiers"
    - "understanding hidden memories of recurrent neural networks"
    - "towards better analysis of deep convolutional neural networks"
    - "graying the black box understanding dqns"
    - "hyperband a novel bandit based approach to hyperparameter optimization"
    - "neural architecture search with reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "analyzing the noise robustness of deep neural networks"):
  with members:
    - "gan lab understanding complex deep generative models using interactive visual experimentation"
    - "retainvis visual analytics with interpretable and interactive recurrent neural networks on electronic medical records"
    - "explaining vulnerabilities to adversarial machine learning through visual analytics"
    - "advhat real world adversarial attack on arcface face id system"
    - "visual analytics in deep learning an interrogative survey for the next frontiers"
    - "do convolutional neural networks learn class hierarchy"
    - "visual analytics for explainable deep learning"
    - "recent research advances on interactive machine learning"

Connected through hyperedge HE-6 (induced by source paper "embeddingvis a visual analytics approach to comparative network embedding inspection"):
  with members:
    - "attributed social network embedding"
    - "struc2vec learning node representations from structural identity"
    - "attributed network embedding for learning in a dynamic environment"
    - "a comprehensive survey of graph embedding problems techniques and applications"
    - "ssp semantic space projection for knowledge graph embedding with text descriptions"
    - "pixelsne visualizing fast with just enough precision via pixel aligned stochastic neighbor embedding"
    - "node2vec scalable feature learning for networks"
    - "line large scale information network embedding"

Connected through hyperedge HE-7 (induced by source paper "rulematrix visualizing and understanding classifiers with rules"):
  with members:
    - "do convolutional neural networks learn class hierarchy"
    - "ai safety gridworlds"
    - "understanding hidden memories of recurrent neural networks"
    - "towards better analysis of machine learning models a visual analytics perspective"
    - "towards better analysis of deep convolutional neural networks"
    - "scalable bayesian rule lists"
    - "why should i trust you explaining the predictions of any classifier"
    - "lstmvis a tool for visual analysis of hidden state dynamics in recurrent 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)