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: "visual analysis of hidden state dynamics in recurrent neural networks"
  Abstract: Recurrent neural networks, and in particular long short-term memory networks (LSTMs), 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 a user 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 domain specific structural annotations. We further show several use cases of the tool for analyzing specific hidden state properties on datasets containing nesting, phrase structure, and chord progressions, and demonstrate how the tool can be used to isolate patterns for further statistical analysis.

Connected through hyperedge HE-1 (induced by source paper "the what if tool interactive probing of machine learning models"):
  with members:
    - "rulematrix visualizing and understanding classifiers with rules"
    - "visual analytics in deep learning an interrogative survey for the next frontiers"
    - "ai fairness 360 an extensible toolkit for detecting understanding and mitigating unwanted algorithmic bias"
    - "smoothgrad removing noise by adding noise"
    - "counterfactual explanations without opening the black box automated decisions and the gdpr"
    - "towards better analysis of deep convolutional neural networks"
    - "equality of opportunity in supervised learning"
    - "why should i trust you explaining the predictions of any classifier"

Connected through hyperedge HE-2 (induced by source paper "state regularized recurrent neural networks"):
  with members:
    - "context free transductions with neural stacks"
    - "on the practical computational power of finite precision rnns for language recognition"
    - "learning longer term dependencies in rnns with auxiliary losses"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "an empirical evaluation of rule extraction from recurrent neural networks"
    - "attention is all you need"
    - "frustratingly short attention spans in neural language modeling"
    - "automatic rule extraction from long short term memory networks"

Connected through hyperedge HE-3 (induced by source paper "x tom explaining with theory of mind for gaining justified human trust"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "attention is not explanation"
    - "counterfactual visual explanations"
    - "metrics for explainable ai challenges and prospects"
    - "explanations based on the missing towards contrastive explanations with pertinent negatives"
    - "smoothgrad removing noise by adding noise"
    - "axiomatic attribution for deep networks"
    - "interpretable convolutional neural networks"

Connected through hyperedge HE-4 (induced by source paper "reverse engineering recurrent networks for sentiment classification reveals line attractor dynamics"):
  with members:
    - "deep learning for sentiment analysis a survey"
    - "beyond word importance contextual decomposition to extract interactions from lstms"
    - "a unified approach to interpreting model predictions"
    - "visualizing and understanding recurrent networks"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

Connected through hyperedge HE-5 (induced by source paper "deepbase deep inspection of neural networks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "analyzing and interpreting neural networks for nlp a report on the first blackboxnlp workshop"
    - "visual analytics in deep learning an interrogative survey for the next frontiers"
    - "local explanation methods for deep neural networks lack sensitivity to parameter values"
    - "learning to generate reviews and discovering sentiment"
    - "network dissection quantifying interpretability of deep visual representations"
    - "robustfill neural program learning under noisy i o"
    - "a causal framework for explaining the predictions of black box sequence to sequence models"

Connected through hyperedge HE-6 (induced by source paper "do convolutional neural networks learn class hierarchy"):
  with members:
    - "towards better analysis of machine learning models a visual analytics perspective"
    - "direct manipulation visualization of deep networks"
    - "towards better analysis of deep convolutional neural networks"
    - "synthesizing the preferred inputs for neurons in neural networks via deep generator networks"
    - "multifaceted feature visualization uncovering the different types of features learned by each neuron in deep neural networks"
    - "decision forests convolutional networks and the models in between"
    - "why should i trust you explaining the predictions of any classifier"
    - "visualizing deep convolutional neural networks using natural pre images"

Connected through hyperedge HE-7 (induced by source paper "translating neuralese"):
  with members:
    - "context aware captions from context agnostic supervision"
    - "inferring logical forms from denotations"
    - "learning to generate compositional color descriptions"
    - "a joint speaker listener reinforcer model for referring expressions"
    - "generating visual explanations"
    - "why should i trust you explaining the predictions of any classifier"
    - "reasoning about pragmatics with neural listeners and speakers"
    - "towards multi agent communication based language learning"

Connected through hyperedge HE-8 (induced by source paper "understanding grounded language learning agents"):
  with members:
    - "from language to goals inverse reinforcement learning for vision based instruction following"
    - "mapping instructions to actions in 3d environments with visual goal prediction"
    - "learning to understand goal specifications by modelling reward"
    - "neural modular control for embodied question answering"
    - "gated attention architectures for task oriented language grounding"
    - "zero shot task generalization with multi task deep reinforcement learning"
    - "mapping instructions and visual observations to actions with reinforcement learning"
    - "grounded language learning in a simulated 3d world"

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)