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: "explainable ai for trees from local explanations to global understanding"
  Abstract: Tree-based machine learning models such as random forests, decision trees, and gradient boosted trees are the most popular non-linear predictive models used in practice today, yet comparatively little attention has been paid to explaining their predictions. Here we significantly improve the interpretability of tree-based models through three main contributions: 1) The first polynomial time algorithm to compute optimal explanations based on game theory. 2) A new type of explanation that directly measures local feature interaction effects. 3) A new set of tools for understanding global model structure based on combining many local explanations of each prediction. We apply these tools to three medical machine learning problems and show how combining many high-quality local explanations allows us to represent global structure while retaining local faithfulness to the original model. These tools enable us to i) identify high magnitude but low frequency non-linear mortality risk factors in the general US population, ii) highlight distinct population sub-groups with shared risk characteristics, iii) identify non-linear interaction effects among risk factors for chronic kidney disease, and iv) monitor a machine learning model deployed in a hospital by identifying which features are degrading the model's performance over time. Given the popularity of tree-based machine learning models, these improvements to their interpretability have implications across a broad set of domains.

Connected through hyperedge HE-1 (induced by source paper "vulnerabilities of connectionist ai applications evaluation and defence"):
  with members:
    - "towards ground truth evaluation of visual explanations"
    - "continual lifelong learning with neural networks a review"
    - "programmable neural network trojan for pre trained feature extractor"
    - "hidden trigger backdoor attacks"
    - "adversarial attacks and defenses in images graphs and text a review"
    - "on evaluating adversarial robustness"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "detecting backdoor attacks on deep neural networks by activation clustering"

Connected through hyperedge HE-2 (induced by source paper "fairness by explicability and adversarial shap learning"):
  with members:
    - "counterfactual fairness removing direct effects through regularization"
    - "explaining machine learning classifiers through diverse counterfactual explanations"
    - "a framework for understanding unintended consequences of machine learning"
    - "a survey on bias and fairness in machine learning"
    - "a neural network framework for fair classifier"
    - "discriminative but not discriminatory a comparison of fairness definitions under different worldviews"
    - "mitigating unwanted biases with adversarial learning"
    - "classification with fairness constraints a meta algorithm with provable guarantees"

Connected through hyperedge HE-3 (induced by source paper "a framework for democratizing ai"):
  with members:
    - "a generic framework for privacy preserving deep learning"
    - "deep neural decision trees"
    - "auto keras an efficient neural architecture search system"
    - "tune a research platform for distributed model selection and training"
    - "ray a distributed framework for emerging ai applications"
    - "xgboost a scalable tree boosting system"
    - "why should i trust you explaining the predictions of any classifier"
    - "mxnet a flexible and efficient machine learning library for heterogeneous distributed systems"

Connected through hyperedge HE-4 (induced by source paper "lionforests local interpretation of random forests"):
  with members:
    - "lionets local interpretation of neural networks through penultimate layer decoding"
    - "techniques for interpretable machine learning"
    - "explainable artificial intelligence understanding visualizing and interpreting deep learning models"
    - "a unified approach to interpreting model predictions"
    - "rule extraction algorithm for deep neural networks a review"
    - "xgboost a scalable tree boosting system"
    - "the mythos of model interpretability"
    - "why should i trust you explaining the predictions of any classifier"

Connected through hyperedge HE-5 (induced by source paper "causality based explanation of classification outcomes"):
  with members:
    - "the shapley value of tuples in query answering"
    - "explaining classifiers with causal concept effect cace"
    - "an interpretable model with globally consistent explanations for credit risk"
    - "a unified approach to interpreting model predictions"
    - "the mythos of model interpretability"
    - "why should i trust you explaining the predictions of any classifier"
    - "the complexity of causality and responsibility for query answers and non answers"
    - "responsibility and blame a structural model approach"

Connected through hyperedge HE-6 (induced by source paper "deep transfer learning for physiological signals"):
  with members:
    - "transfer learning for clinical time series analysis using recurrent neural networks"
    - "deep transfer learning for eeg based brain computer interface"
    - "multisource transfer learning with convolutional neural networks for lung pattern analysis"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "a unified approach to interpreting model predictions"
    - "deep convolutional neural networks for computer aided detection cnn architectures dataset characteristics and transfer learning"
    - "gram graph based attention model for healthcare representation learning"
    - "frustratingly easy domain adaptation"

Connected through hyperedge HE-7 (induced by source paper "explainable artificial intelligence model to predict acute critical illness from electronic health records"):
  with members:
    - "stcn stochastic temporal convolutional networks"
    - "deepsofa a continuous acuity score for critically ill patients using clinically interpretable deep learning"
    - "early recognition of sepsis with gaussian process temporal convolutional networks and dynamic time warping"
    - "early detection of sepsis utilizing deep learning on electronic health record event sequences"
    - "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"
    - "innvestigate neural networks"
    - "methods for interpreting and understanding deep neural networks"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"

Connected through hyperedge HE-8 (induced by source paper "explaining models by propagating shapley values of local components"):
  with members:
    - "explaining deep neural networks with a polynomial time algorithm for shapley values approximation"
    - "generalized integrated gradients a practical method for explaining diverse ensembles"
    - "learning explainable models using attribution priors"
    - "the many shapley values for model explanation"
    - "axiomatic attribution for deep networks"
    - "a unified approach to interpreting model predictions"
    - "learning important features through propagating activation differences"
    - "towards better understanding of gradient based attribution methods for deep neural networks"

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)