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: "on the in fidelity and sensitivity for explanations"
  Abstract: We consider objective evaluation measures of explanations of complex black-box machine learning models. We propose simple robust variants of two notions that have been considered in recent literature: (in)fidelity, and sensitivity. We analyze optimal explanations with respect to both these measures, and while the optimal explanation for sensitivity is a vacuous constant explanation, that for our notion of infidelity is a novel combination of two popular explanation methods. Another salient question given these measures is how to modify \emph{any given explanation} to have better values with respect to these measures. We propose a simple modification based on lowering sensitivity, and moreover show that when done appropriately, we could simultaneously improve both sensitivity as well as fidelity.

Connected through hyperedge HE-1 (induced by source paper "self explaining ai as an alternative to interpretable ai"):
  with members:
    - "a jamming transition from under to over parametrization affects generalization in deep learning"
    - "two models of double descent for weak features"
    - "one explanation does not fit all a toolkit and taxonomy of ai explainability techniques"
    - "deep double descent where bigger models and more data hurt"
    - "identifying weights and architectures of unknown relu networks"
    - "knowing what you know in brain segmentation using bayesian deep neural networks"
    - "an interpretable deep hierarchical semantic convolutional neural network for lung nodule malignancy classification"
    - "sanity checks for saliency maps"

Connected through hyperedge HE-2 (induced by source paper "analysis of explainers of black box deep neural networks for computer vision a survey"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "equalizing gender biases in neural machine translation with word embeddings techniques"
    - "understanding deep networks via extremal perturbations and smooth masks"
    - "summit scaling deep learning interpretability by visualizing activation and attribution summarizations"
    - "attacking optical flow"
    - "a general framework for adversarial examples with objectives"
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "visualbackprop efficient visualization of cnns"

Connected through hyperedge HE-3 (induced by source paper "ml loo detecting adversarial examples with feature attribution"):
  with members:
    - "adv bnn improved adversarial defense through robust bayesian neural network"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "training verified learners with learned verifiers"
    - "rob gan generator discriminator and adversarial attacker"
    - "adversarial learning and explainability in structured datasets"
    - "attacks meet interpretability attribute steered detection of adversarial samples"
    - "concise and stable explanations using adversarial training"
    - "on the limitation of local intrinsic dimensionality for characterizing the subspaces of adversarial examples"

Connected through hyperedge HE-4 (induced by source paper "explainable machine learning in deployment"):
  with members:
    - "the what if tool interactive probing of machine learning models"
    - "understanding impacts of high order loss approximations and features in deep learning interpretation"
    - "certifai counterfactual explanations for robustness transparency interpretability and fairness of artificial intelligence models"
    - "transparency motivations and challenges"
    - "automatic model monitoring for data streams"
    - "why should you trust my explanation understanding uncertainty in lime explanations"
    - "neural network attributions a causal perspective"
    - "explaining explanations in ai"

Connected through hyperedge HE-5 (induced by source paper "evaluating explanation without ground truth in interpretable machine learning"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "an evaluation of the human interpretability of explanation"
    - "learning interpretable models with causal guarantees"
    - "interpretable deep learning in drug discovery"
    - "on attribution of recurrent neural network predictions via additive decomposition"
    - "neural network attributions a causal perspective"
    - "interpreting neural networks using flip points"
    - "towards explanation of dnn based prediction with guided feature inversion"

Connected through hyperedge HE-6 (induced by source paper "meta decision trees for explainable recommendation systems"):
  with members:
    - "explainable recommendation a survey and new perspectives"
    - "adaptive neural trees"
    - "on the robustness of interpretability methods"
    - "geometric matrix completion with recurrent multi graph neural networks"
    - "a unified approach to interpreting model predictions"
    - "a simple neural attentive meta learner"
    - "distilling a neural network into a soft decision tree"
    - "why should i trust you explaining the predictions of any classifier"

Connected through hyperedge HE-7 (induced by source paper "do not trust additive explanations"):
  with members:
    - "consistent individualized feature attribution for tree ensembles"
    - "enslaving the algorithm from a right to an explanation to a right to better decisions"
    - "on the robustness of interpretability methods"
    - "counterfactual explanations without opening the black box automated decisions and the gdpr"
    - "a unified approach to interpreting model predictions"
    - "learning important features through propagating activation differences"
    - "why should i trust you explaining the predictions of any classifier"
    - "openml networked science in machine learning"

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)