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: "gan lab understanding complex deep generative models using interactive visual experimentation"
  Abstract: Recent success in deep learning has generated immense interest among practitioners and students, inspiring many to learn about this new technology. While visual and interactive approaches have been successfully developed to help people more easily learn deep learning, most existing tools focus on simpler models. In this work, we present GAN Lab, the first interactive visualization tool designed for non-experts to learn and experiment with  Generative Adversarial Networks  (GANs), a popular class of complex deep learning models. With GAN Lab, users can interactively train generative models and visualize the dynamic training process's intermediate results. GAN Lab tightly integrates an  model overview graph  that summarizes GAN's structure, and a layered distributions view that helps users interpret the interplay between submodels. GAN Lab introduces new interactive experimentation features for learning complex deep learning models, such as  step-by-step  training at multiple levels of abstraction for understanding intricate training dynamics. Implemented using  TensorFlow.js , GAN Lab is accessible to anyone via modern web browsers, without the need for installation or specialized hardware, overcoming a major practical challenge in deploying interactive tools for deep learning.

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"
    - "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"
    - "innvestigate neural networks"

Connected through hyperedge HE-2 (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"
    - "manifold a model agnostic framework for interpretation and diagnosis of machine learning models"
    - "deeptracker visualizing the training process of convolutional neural networks"
    - "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-3 (induced by source paper "nnva neural network assisted visual analysis of yeast cell polarization simulation"):
  with members:
    - "visual analytics in deep learning an interrogative survey for the next frontiers"
    - "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-4 (induced by source paper "cnn 101 interactive visual learning for convolutional neural networks"):
  with members:
    - "tensorflow js machine learning for the web and beyond"
    - "visual analytics in deep learning an interrogative survey for the next frontiers"
    - "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-5 (induced by source paper "visual evaluation of generative adversarial networks for time series data"):
  with members:
    - "mad gan multivariate anomaly detection for time series data with generative adversarial networks"
    - "visual analytics in deep learning an interrogative survey for the next frontiers"
    - "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-6 (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"
    - "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"
    - "neural architecture search with bayesian optimisation and optimal transport"

Connected through hyperedge HE-7 (induced by source paper "summit scaling deep learning interpretability by visualizing activation and attribution summarizations"):
  with members:
    - "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"
    - "do convolutional neural networks learn class hierarchy"
    - "explaining explanations an overview of interpretability of machine learning"
    - "analyzing the noise robustness of deep neural networks"

Connected through hyperedge HE-8 (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"
    - "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"
    - "bert pre training of deep bidirectional transformers for language understanding"

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)