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: "manifold a model agnostic framework for interpretation and diagnosis of machine learning models"
  Abstract: Interpretation and diagnosis of machine learning models have gained renewed interest in recent years with breakthroughs in new approaches. We present Manifold, a framework that utilizes visual analysis techniques to support interpretation, debugging, and comparison of machine learning models in a more transparent and interactive manner. Conventional techniques usually focus on visualizing the internal logic of a specific model type (i.e., deep neural networks), lacking the ability to extend to a more complex scenario where different model types are integrated. To this end, Manifold is designed as a generic framework that does not rely on or access the internal logic of the model and solely observes the input (i.e., instances or features) and the output (i.e., the predicted result and probability distribution). We describe the workflow of Manifold as an iterative process consisting of three major phases that are commonly involved in the model development and diagnosis process: inspection (hypothesis), explanation (reasoning), and refinement (verification). The visual components supporting these tasks include a scatterplot-based visual summary that overviews the models' outcome and a customizable tabular view that reveals feature discrimination. We demonstrate current applications of the framework on the classification and regression tasks and discuss other potential machine learning use scenarios where Manifold can be applied.

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"
    - "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 "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"
    - "predicting purchasing intent automatic feature learning using recurrent neural networks"
    - "do convolutional neural networks learn class hierarchy"
    - "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-3 (induced by source paper "fairsight visual analytics for fairness in decision making"):
  with members:
    - "improving fairness in machine learning systems what do industry practitioners need"
    - "a nutritional label for rankings"
    - "using image fairness representations in diversity based re ranking for recommendations"
    - "rulematrix visualizing and understanding classifiers with rules"
    - "equity of attention amortizing individual fairness in rankings"
    - "fairness of exposure in rankings"
    - "fa ir a fair top k ranking algorithm"
    - "proxy non discrimination in data driven systems"

Connected through hyperedge HE-4 (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"
    - "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-5 (induced by source paper "vice visual counterfactual explanations for machine learning models"):
  with members:
    - "the what if tool interactive probing of machine learning models"
    - "explaining machine learning classifiers through diverse counterfactual explanations"
    - "rulematrix visualizing and understanding classifiers with rules"
    - "a survey of methods for explaining black box models"
    - "manipulating and measuring model interpretability"
    - "explanation in artificial intelligence insights from the social sciences"
    - "counterfactual explanations without opening the black box automated decisions and the gdpr"
    - "a unified approach to interpreting model predictions"

Connected through hyperedge HE-6 (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"
    - "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"
    - "cnncomparator comparative analytics of convolutional neural networks"

Connected through hyperedge HE-7 (induced by source paper "a user based visual analytics workflow for exploratory model analysis"):
  with members:
    - "clustrophile 2 guided visual clustering analysis"
    - "prediction factory automated development and collaborative evaluation of predictive models"
    - "do convolutional neural networks learn class hierarchy"
    - "rnnbow visualizing learning via backpropagation gradients in rnns"
    - "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-8 (induced by source paper "atmseer increasing transparency and controllability in automated machine learning"):
  with members:
    - "deeptracker visualizing the training process of convolutional neural networks"
    - "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"

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)