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: "retainvis visual analytics with interpretable and interactive recurrent neural networks on electronic medical records"
  Abstract: We have recently seen many successful applications of recurrent neural networks (RNNs) on electronic medical records (EMRs), which contain histories of patients' diagnoses, medications, and other various events, in order to predict the current and future states of patients. Despite the strong performance of RNNs, it is often challenging for users to understand why the model makes a particular prediction. Such black-box nature of RNNs can impede its wide adoption in clinical practice. Furthermore, we have no established methods to interactively leverage users' domain expertise and prior knowledge as inputs for steering the model. Therefore, our design study aims to provide a visual analytics solution to increase interpretability and interactivity of RNNs via a joint effort of medical experts, artificial intelligence scientists, and visual analytics researchers. Following the iterative design process between the experts, we design, implement, and evaluate a visual analytics tool called RetainVis, which couples a newly improved, interpretable, and interactive RNN-based model called RetainEX and visualizations for users' exploration of EMR data in the context of prediction tasks. Our study shows the effective use of RetainVis for gaining insights into how individual medical codes contribute to making risk predictions, using EMRs of patients with heart failure and cataract symptoms. Our study also demonstrates how we made substantial changes to the state-of-the-art RNN model called RETAIN in order to make use of temporal information and increase interactivity. This study will provide a useful guideline for researchers that aim to design an interpretable and interactive visual analytics tool for RNNs.

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"
    - "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 "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"
    - "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"
    - "learning important features through propagating activation differences"

Connected through hyperedge HE-3 (induced by source paper "do ml experts discuss explainability for ai systems a discussion case in the industry for a domain specific solution"):
  with members:
    - "mediation challenges and socio technical gaps for explainable deep learning applications"
    - "atmseer increasing transparency and controllability in automated machine learning"
    - "interactive naming for explaining deep neural networks a formative study"
    - "explanation in artificial intelligence insights from the social sciences"

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"
    - "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"
    - "gan dissection visualizing and understanding generative adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "time2vec learning a vector representation of time"):
  with members:
    - "relational representation learning for dynamic knowledge graphs a survey"
    - "learning temporal point processes via reinforcement learning"
    - "neural decomposition of time series data for effective generalization"
    - "can recurrent neural networks warp time"
    - "deep reinforcement learning of marked temporal point processes"
    - "streaming graph neural networks"
    - "neural ordinary differential equations"
    - "learning dynamic embeddings from temporal interactions"

Connected through hyperedge HE-6 (induced by source paper "what clinicians want contextualizing explainable machine learning for clinical end use"):
  with members:
    - "model cards for model reporting"
    - "attention is not explanation"
    - "human centered tools for coping with imperfect algorithms during medical decision making"
    - "raim recurrent attentive and intensive model of multimodal patient monitoring data"
    - "opportunities in machine learning for healthcare"
    - "clinicalvis supporting clinical task focused design evaluation"
    - "how do humans understand explanations from machine learning systems an evaluation of the human interpretability of explanation"
    - "manipulating and measuring model interpretability"

Connected through hyperedge HE-7 (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"
    - "gan lab understanding complex deep generative models using interactive visual experimentation"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "tx ray quantifying and explaining model knowledge transfer in un supervised nlp"):
  with members:
    - "episodic memory in lifelong language learning"
    - "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"
    - "are sixteen heads really better than one"
    - "analyzing multi head self attention specialized heads do the heavy lifting the rest can be pruned"
    - "continual lifelong learning with neural networks a review"
    - "to tune or not to tune adapting pretrained representations to diverse tasks"
    - "visual interaction with deep learning models through collaborative semantic inference"
    - "universal adversarial triggers for attacking and analyzing nlp"

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)