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: "neural network attributions a causal perspective"
  Abstract: We propose a new attribution method for neural networks developed using first principles of causality (to the best of our knowledge, the first such). The neural network architecture is viewed as a Structural Causal Model, and a methodology to compute the causal effect of each feature on the output is presented. With reasonable assumptions on the causal structure of the input data, we propose algorithms to efficiently compute the causal effects, as well as scale the approach to data with large dimensionality. We also show how this method can be used for recurrent neural networks. We report experimental results on both simulated and real datasets showcasing the promise and usefulness of the proposed algorithm.

Connected through hyperedge HE-1 (induced by source paper "causal interpretability for machine learning problems methods and evaluation"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "explaining machine learning classifiers through diverse counterfactual explanations"
    - "explainable reinforcement learning through a causal lens"
    - "learning interpretable models with causal guarantees"
    - "explaining classifiers with causal concept effect cace"
    - "on the accuracy of influence functions for measuring group effects"
    - "explaining deep learning models with constrained adversarial examples"
    - "incorporating interpretability into latent factor models via fast influence analysis"

Connected through hyperedge HE-2 (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"
    - "explaining explanations in ai"
    - "on the in fidelity and sensitivity for explanations"

Connected through hyperedge HE-3 (induced by source paper "xoc explainable observer classifier for explainable binary decisions"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "full gradient representation for neural network visualization"
    - "cxplain causal explanations for model interpretation under uncertainty"
    - "learning attentional communication for multi agent cooperation"
    - "grounding visual explanations"
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "tarmac targeted multi agent communication"
    - "interpreting cnns via decision trees"

Connected through hyperedge HE-4 (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"
    - "on the in fidelity and sensitivity for explanations"
    - "interpreting neural networks using flip points"
    - "towards explanation of dnn based prediction with guided feature inversion"

Connected through hyperedge HE-5 (induced by source paper "cxplain causal explanations for model interpretation under uncertainty"):
  with members:
    - "why should you trust my explanation understanding uncertainty in lime explanations"
    - "what to expect of classifiers reasoning about logistic regression with missing features"
    - "explaining deep learning models a bayesian non parametric approach"
    - "granger causal attentive mixtures of experts learning important features with neural networks"
    - "sanity checks for saliency maps"
    - "smoothgrad removing noise by adding noise"
    - "self normalizing neural networks"
    - "beat by beat classifying cardiac arrhythmias with recurrent neural networks"

Connected through hyperedge HE-6 (induced by source paper "xrai better attributions through regions"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "review of visual saliency detection with comprehensive information"
    - "a note about local explanation methods for deep neural networks lack sensitivity to parameter values"
    - "what made you do this understanding black box decisions with sufficient input subsets"
    - "sanity checks for saliency maps"
    - "explaining nonlinear classification decisions with deep taylor decomposition"
    - "axiomatic attribution for deep networks"
    - "learning important features through propagating activation differences"

Connected through hyperedge HE-7 (induced by source paper "segment integrated gradients better attributions through regions"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "a note about local explanation methods for deep neural networks lack sensitivity to parameter values"
    - "what made you do this understanding black box decisions with sufficient input subsets"
    - "sanity checks for saliency maps"
    - "smoothgrad removing noise by adding noise"
    - "axiomatic attribution for deep networks"
    - "learning important features through propagating activation differences"
    - "visualizing deep neural network decisions prediction difference analysis"

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)