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: "lstmvis a tool for visual analysis of hidden state dynamics in recurrent neural networks"
  Abstract: Recurrent neural networks, and in particular long short-term memory (LSTM) networks, are a remarkably effective tool for sequence modeling that learn a dense black-box hidden representation of their sequential input. Researchers interested in better understanding these models have studied the changes in hidden state representations over time and noticed some interpretable patterns but also significant noise. In this work, we present LSTMVIS, a visual analysis tool for recurrent neural networks with a focus on understanding these hidden state dynamics. The tool allows users to select a hypothesis input range to focus on local state changes, to match these states changes to similar patterns in a large data set, and to align these results with structural annotations from their domain. We show several use cases of the tool for analyzing specific hidden state properties on dataset containing nesting, phrase structure, and chord progressions, and demonstrate how the tool can be used to isolate patterns for further statistical analysis. We characterize the domain, the different stakeholders, and their goals and tasks.

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 "what went wrong and when instance wise feature importance for time series models"):
  with members:
    - "is attention interpretable"
    - "what clinicians want contextualizing explainable machine learning for clinical end use"
    - "attention is not explanation"
    - "tsviz demystification of deep learning models for time series analysis"
    - "explaining an increase in predicted risk for clinical alerts"
    - "an interpretable lstm neural network for autoregressive exogenous model"
    - "raim recurrent attentive and intensive model of multimodal patient monitoring data"
    - "explaining image classifiers by counterfactual generation"

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"
    - "do convolutional neural networks learn class hierarchy"
    - "understanding hidden memories of recurrent neural networks"
    - "a unified approach to interpreting model predictions"

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"
    - "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"

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"
    - "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"

Connected through hyperedge HE-6 (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-7 (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-8 (induced by source paper "interrogating the explanatory power of attention in neural machine translation"):
  with members:
    - "saliency driven word alignment interpretation for neural machine translation"
    - "model explanations under calibration"
    - "an analysis of attention over clinical notes for predictive tasks"
    - "analyzing the structure of attention in a transformer language model"
    - "attention is not explanation"
    - "do transformer attention heads provide transparency in abstractive summarization"
    - "what does bert look at an analysis of bert s attention"
    - "interpreting recurrent and attention based neural models a case study on natural language inference"

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.CL(Computation and Language)