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: "finding influential training samples for gradient boosted decision trees"
  Abstract: We address the problem of finding influential training samples for a particular case of tree ensemble-based models, e.g., Random Forest (RF) or Gradient Boosted Decision Trees (GBDT). A natural way of formalizing this problem is studying how the model's predictions change upon leave-one-out retraining, leaving out each individual training sample. Recent work has shown that, for parametric models, this analysis can be conducted in a computationally efficient way. We propose several ways of extending this framework to non-parametric GBDT ensembles under the assumption that tree structures remain fixed. Furthermore, we introduce a general scheme of obtaining further approximations to our method that balance the trade-off between performance and computational complexity. We evaluate our approaches on various experimental setups and use-case scenarios and demonstrate both the quality of our approach to finding influential training samples in comparison to the baselines and its computational efficiency.

Connected through hyperedge HE-1 (induced by source paper "why does my model fail contrastive local explanations for retail forecasting"):
  with members:
    - "efficient search for diverse coherent explanations"
    - "explainable ai for trees from local explanations to global understanding"
    - "grounding visual explanations"
    - "explanations based on the missing towards contrastive explanations with pertinent negatives"
    - "a survey of methods for explaining black box models"
    - "explanation in artificial intelligence insights from the social sciences"
    - "counterfactual explanations without opening the black box automated decisions and the gdpr"
    - "a unified approach to interpreting model predictions"

Connected through hyperedge HE-2 (induced by source paper "less is better unweighted data subsampling via influence function"):
  with members:
    - "theoretically principled trade off between robustness and accuracy"
    - "data dropout optimizing training data for convolutional neural networks"
    - "training set debugging using trusted items"
    - "learning to reweight examples for robust deep learning"
    - "focal loss for dense object detection"
    - "axiomatic characterization of data driven influence measures for classification"
    - "recommendations as treatments debiasing learning and evaluation"
    - "intriguing properties of neural networks"

Connected through hyperedge HE-3 (induced by source paper "towards efficient data valuation based on the shapley value"):
  with members:
    - "a unified approach to interpreting model predictions"
    - "towards evaluating the robustness of neural networks"
    - "bounding the estimation error of sampling based shapley value approximation"

Connected through hyperedge HE-4 (induced by source paper "faithfully explaining rankings in a news recommender system"):
  with members:
    - "right for the right reasons training differentiable models by constraining their explanations"
    - "generating visual explanations"
    - "why should i trust you explaining the predictions of any classifier"
    - "adam a method for stochastic optimization"

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)