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: "rnnbow visualizing learning via backpropagation gradients in rnns"
  Abstract: We present RNNbow, an interactive tool for visualizing the gradient flow during backpropagation in training of recurrent neural networks. By visualizing the gradient, as opposed to activations, RNNbow offers insight into how the network is learning. We show how it illustrates the vanishing gradient and the training process.

Connected through hyperedge HE-1 (induced by source paper "visual interaction with deep learning models through collaborative semantic inference"):
  with members:
    - "on human predictions with explanations and predictions of machine learning models a case study on deception detection"
    - "retainvis visual analytics with interpretable and interactive recurrent neural networks on electronic medical records"
    - "semantic image synthesis with spatially adaptive normalization"
    - "improving fairness in machine learning systems what do industry practitioners need"
    - "a tutorial on deep latent variable models of natural language"
    - "visual analytics in deep learning an interrogative survey for the next frontiers"
    - "bottom up abstractive summarization"
    - "what is one grain of sand in the desert analyzing individual neurons in deep nlp models"

Connected through hyperedge HE-2 (induced by source paper "ablate variate and contemplate visual analytics for discovering neural architectures"):
  with members:
    - "a user based visual analytics workflow for exploratory model analysis"
    - "gan lab understanding complex deep generative models using interactive visual experimentation"
    - "clustrophile 2 guided visual clustering analysis"
    - "visual analytics in deep learning an interrogative survey for the next frontiers"
    - "regularized evolution for image classifier architecture search"
    - "auto keras an efficient neural architecture search system"
    - "darts differentiable architecture search"
    - "object detection with deep learning a review"

Connected through hyperedge HE-3 (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"
    - "deeptracker visualizing the training process of convolutional neural networks"
    - "do convolutional neural networks learn class hierarchy"

Connected through hyperedge HE-4 (induced by source paper "a user based visual analytics workflow for exploratory model analysis"):
  with members:
    - "manifold a model agnostic framework for interpretation and diagnosis of machine learning models"
    - "clustrophile 2 guided visual clustering analysis"
    - "prediction factory automated development and collaborative evaluation of predictive models"
    - "do convolutional neural networks learn class hierarchy"
    - "towards better analysis of machine learning models a visual analytics perspective"
    - "towards better analysis of deep convolutional neural networks"
    - "what may visualization processes optimize"
    - "the mythos of model interpretability"

Connected through hyperedge HE-5 (induced by source paper "visualizing rnn states with predictive semantic encodings"):
  with members:
    - "long short term memory as a dynamically computed element wise weighted sum"
    - "understanding hidden memories of recurrent neural networks"
    - "why should i trust you explaining the predictions of any classifier"
    - "understanding neural networks through representation erasure"
    - "lstmvis a tool for visual analysis of hidden state dynamics in recurrent neural networks"
    - "man is to computer programmer as woman is to homemaker debiasing word embeddings"
    - "visualizing and understanding neural models in nlp"
    - "visualizing and understanding recurrent networks"

Connected through hyperedge HE-6 (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"
    - "visual analytics for explainable deep learning"
    - "shield fast practical defense and vaccination for deep learning using jpeg compression"

Connected through hyperedge HE-7 (induced by source paper "seq2seq vis a visual debugging tool for sequence to sequence models"):
  with members:
    - "achieving human parity on automatic chinese to english news translation"
    - "generating wikipedia by summarizing long sequences"
    - "visual analytics in deep learning an interrogative survey for the next frontiers"
    - "how do humans understand explanations from machine learning systems an evaluation of the human interpretability of explanation"
    - "a causal framework for explaining the predictions of black box sequence to sequence models"
    - "attention is all you need"
    - "billion scale similarity search with gpus"
    - "picasso a modular framework for visualizing the learning process of neural network image classifiers"

Connected through hyperedge HE-8 (induced by source paper "neuralcubes deep representations for visual data exploration"):
  with members:
    - "a model for learned bloom filters and related structures"
    - "urban pulse capturing the rhythm of cities"
    - "the case for learned index structures"
    - "dynamic routing between capsules"
    - "improving generalization performance by switching from adam to sgd"
    - "representation learning by learning to count"
    - "towards better analysis of deep convolutional neural networks"
    - "unsupervised learning of visual representations by solving jigsaw puzzles"

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.LG(Machine Learning)