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: "direct manipulation visualization of deep networks"
  Abstract: The recent successes of deep learning have led to a wave of interest from non-experts. Gaining an understanding of this technology, however, is difficult. While the theory is important, it is also helpful for novices to develop an intuitive feel for the effect of different hyperparameters and structural variations. We describe TensorFlow Playground, an interactive, open sourced visualization that allows users to experiment via direct manipulation rather than coding, enabling them to quickly build an intuition about neural nets.

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 "cnn 101 interactive visual learning for convolutional neural networks"):
  with members:
    - "tensorflow js machine learning for the web and beyond"
    - "gan lab understanding complex deep generative models using interactive visual experimentation"
    - "visual analytics in deep learning an interrogative survey for the next frontiers"
    - "tensorflow a system for large scale machine learning"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-3 (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-4 (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-5 (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"
    - "do convolutional neural networks learn class hierarchy"
    - "explaining explanations an overview of interpretability of machine learning"

Connected through hyperedge HE-6 (induced by source paper "retainvis visual analytics with interpretable and interactive recurrent neural networks on electronic medical records"):
  with members:
    - "exploring dimensionality reductions with forward and backward projections"
    - "brain tumor segmentation with deep neural networks"
    - "understanding hidden memories of recurrent neural networks"
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"
    - "visualizing deep neural network decisions prediction difference analysis"
    - "dipole diagnosis prediction in healthcare via attention based bidirectional recurrent neural networks"
    - "towards better analysis of deep convolutional neural networks"
    - "the mythos of model interpretability"

Connected through hyperedge HE-7 (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"
    - "deeptracker visualizing the training process of convolutional neural networks"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "how do mixture density rnns predict the future"):
  with members:
    - "hierarchical long term video prediction without supervision"
    - "recurrent world models facilitate policy evolution"
    - "explainable artificial intelligence understanding visualizing and interpreting deep learning models"
    - "robojam a musical mixture density network for collaborative touchscreen interaction"
    - "a neural representation of sketch drawings"
    - "unsupervised learning for physical interaction through video prediction"
    - "recurrent mixture density network for spatiotemporal visual attention"
    - "understanding neural networks through deep visualization"

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)