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: "do convolutional neural networks learn class hierarchy"
  Abstract: Convolutional Neural Networks (CNNs) currently achieve state-of-the-art accuracy in image classification. With a growing number of classes, the accuracy usually drops as the possibilities of confusion increase. Interestingly, the class confusion patterns follow a hierarchical structure over the classes. We present visual-analytics methods to reveal and analyze this hierarchy of similar classes in relation with CNN-internal data. We found that this hierarchy not only dictates the confusion patterns between the classes, it furthermore dictates the learning behavior of CNNs. In particular, the early layers in these networks develop feature detectors that can separate high-level groups of classes quite well, even after a few training epochs. In contrast, the latter layers require substantially more epochs to develop specialized feature detectors that can separate individual classes. We demonstrate how these insights are key to significant improvement in accuracy by designing hierarchy-aware CNNs that accelerate model convergence and alleviate overfitting. We further demonstrate how our methods help in identifying various quality issues in the training data.

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"
    - "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 "visual summary of value level feature attribution in prediction classes with recurrent neural networks"):
  with members:
    - "interpretable and steerable sequence learning via prototypes"
    - "visual reasoning of feature attribution with deep recurrent neural networks"
    - "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"
    - "predicting purchasing intent automatic feature learning using recurrent neural networks"
    - "understanding hidden memories of recurrent neural networks"
    - "a unified approach to interpreting model predictions"
    - "learning important features through propagating activation differences"

Connected through hyperedge HE-4 (induced by source paper "integrating domain knowledge using hierarchies to improve deep classifiers"):
  with members:
    - "hierarchy based image embeddings for semantic image retrieval"
    - "not just a matter of semantics the relationship between visual similarity and semantic similarity"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "progressive neural architecture search"
    - "vse improving visual semantic embeddings with hard negatives"
    - "the inaturalist species classification and detection dataset"
    - "identity mappings in deep residual networks"
    - "lshtc a benchmark for large scale text classification"

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"
    - "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-6 (induced by source paper "summit scaling deep learning interpretability by visualizing activation and attribution summarizations"):
  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"
    - "taskonomy disentangling task transfer learning"
    - "net2vec quantifying and explaining how concepts are encoded by filters in deep neural networks"
    - "methods for interpreting and understanding deep neural networks"
    - "choose your neuron incorporating domain knowledge through neuron importance"
    - "explaining explanations an overview of interpretability of machine learning"
    - "analyzing the noise robustness of deep neural networks"

Connected through hyperedge HE-7 (induced by source paper "making better mistakes leveraging class hierarchies with deep networks"):
  with members:
    - "integrating domain knowledge using hierarchies to improve deep classifiers"
    - "when does label smoothing help"
    - "hierarchy based image embeddings for semantic image retrieval"
    - "class balanced loss based on effective number of samples"
    - "theoretically principled trade off between robustness and accuracy"
    - "on the convergence of adam and beyond"
    - "focal loss for dense object detection"
    - "attention is all you need"

Connected through hyperedge HE-8 (induced by source paper "decision propagation networks for image classification"):
  with members:
    - "cinic 10 is not imagenet or cifar 10"
    - "ensemble soft margin softmax loss for image classification"
    - "class2str end to end latent hierarchy learning"
    - "deep forest towards an alternative to deep neural networks"
    - "deep convolutional decision jungle for image classification"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "improving pairwise ranking for multi label image classification"
    - "aggregated residual transformations for deep 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)