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: "consistent individualized feature attribution for tree ensembles"
  Abstract: Interpreting predictions from tree ensemble methods such as gradient boosting machines and random forests is important, yet feature attribution for trees is often heuristic and not individualized for each prediction. Here we show that popular feature attribution methods are inconsistent, meaning they can lower a feature's assigned importance when the true impact of that feature actually increases. This is a fundamental problem that casts doubt on any comparison between features. To address it we turn to recent applications of game theory and develop fast exact tree solutions for SHAP (SHapley Additive exPlanation) values, which are the unique consistent and locally accurate attribution values. We then extend SHAP values to interaction effects and define SHAP interaction values. We propose a rich visualization of individualized feature attributions that improves over classic attribution summaries and partial dependence plots, and a unique "supervised" clustering (clustering based on feature attributions). We demonstrate better agreement with human intuition through a user study, exponential improvements in run time, improved clustering performance, and better identification of influential features. An implementation of our algorithm has also been merged into XGBoost and LightGBM, see this http URL for details.

Connected through hyperedge HE-1 (induced by source paper "generating interpretable poverty maps using object detection in satellite images"):
  with members:
    - "predicting economic development using geolocated wikipedia articles"
    - "learning to interpret satellite images in global scale using wikipedia"
    - "efficient object detection in large images using deep reinforcement learning"
    - "xview objects in context in overhead imagery"
    - "yolov3 an incremental improvement"
    - "focal loss for dense object detection"
    - "dssd deconvolutional single shot detector"
    - "a unified approach to interpreting model predictions"

Connected through hyperedge HE-2 (induced by source paper "problems with shapley value based explanations as feature importance measures"):
  with members:
    - "the hidden assumptions behind counterfactual explanations and principal reasons"
    - "fooling lime and shap adversarial attacks on post hoc explanation methods"
    - "explainable machine learning in deployment"
    - "the explanation game explaining machine learning models with cooperative game theory"
    - "trust in data science collaboration translation and accountability in corporate data science projects"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "an investigation of interpretability techniques for deep learning in predictive process analytics"):
  with members:
    - "complication risk profiling in diabetes care a bayesian multi task and feature relationship learning approach"
    - "causal learning and explanation of deep neural networks via autoencoded activations"
    - "explanation in artificial intelligence insights from the social sciences"
    - "interpreting cnn knowledge via an explanatory graph"
    - "a unified approach to interpreting model predictions"
    - "the mythos of model interpretability"
    - "why should i trust you explaining the predictions of any classifier"
    - "recurrent neural network for text classification with multi task learning"

Connected through hyperedge HE-4 (induced by source paper "gradient boosting on decision trees for mortality prediction in transcatheter aortic valve implantation"):
  with members:
    - "catboost gradient boosting with categorical features support"
    - "catboost unbiased boosting with categorical features"
    - "a unified approach to interpreting model predictions"
    - "xgboost a scalable tree boosting system"
    - "scikit learn machine learning in python"

Connected through hyperedge HE-5 (induced by source paper "tabnet attentive interpretable tabular learning"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "global explanations of neural networks mapping the landscape of predictions"
    - "deep neural decision trees"
    - "compositional attention networks for machine reasoning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "adaptive neural trees"
    - "xgboost scalable gpu accelerated learning"
    - "learning to explain an information theoretic perspective on model interpretation"

Connected through hyperedge HE-6 (induced by source paper "incorporating priors with feature attribution on text classification"):
  with members:
    - "counterfactual fairness in text classification through robustness"
    - "explanations based on the missing towards contrastive explanations with pertinent negatives"
    - "mitigating unwanted biases with adversarial learning"
    - "explaining explanations an overview of interpretability of machine learning"
    - "reducing gender bias in abusive language detection"
    - "learning adversarially fair and transferable representations"
    - "did the model understand the question"
    - "sanity checks for saliency maps"

Connected through hyperedge HE-7 (induced by source paper "scalable and generalizable social bot detection through data selection"):
  with members:
    - "real time event detection on social data streams"
    - "rtbust exploiting temporal patterns for botnet detection on twitter"
    - "its all in a name detecting and labeling bots by their name"
    - "arming the public with artificial intelligence to counter social bots"
    - "bot electioneering volume visualizing social bot activity during elections"
    - "cashtag piggybacking uncovering spam and bot activity in stock microblogs on twitter"
    - "lobo evaluation of generalization deficiencies in twitter bot classifiers"
    - "deep neural networks for bot detection"

Connected through hyperedge HE-8 (induced by source paper "a categorisation of post hoc explanations for predictive models"):
  with members:
    - "this looks like that deep learning for interpretable image recognition"
    - "bayesian patchworks an approach to case based reasoning"
    - "global model interpretation via recursive partitioning"
    - "interpretable explorable approximations of black box models"
    - "explanation in artificial intelligence insights from the social sciences"
    - "explaining nonlinear classification decisions with deep taylor decomposition"
    - "developing bug free machine learning systems with formal mathematics"
    - "using program induction to interpret transition system dynamics"

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)