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: "interpreting cnns via decision trees"
  Abstract: This paper presents a method to learn a decision tree to quantitatively explain the logic of each prediction of a pre-trained convolutional neural networks (CNNs). Our method boosts the following two aspects of network interpretability. 1) In the CNN, each filter in a high conv-layer must represent a specific object part, instead of describing mixed patterns without clear meanings. 2) People can explain each specific prediction made by the CNN at the semantic level using a decision tree, i.e., which filters (or object parts) are used for prediction and how much they contribute in the prediction. To conduct such a quantitative explanation of a CNN, our method learns explicit representations of object parts in high conv-layers of the CNN and mines potential decision modes memorized in fully-connected layers. The decision tree organizes these potential decision modes in a coarse-to-fine manner. Experiments have demonstrated the effectiveness of the proposed method.

Connected through hyperedge HE-1 (induced by source paper "leveraging rationales to improve human task performance"):
  with members:
    - "automated rationale generation a technique for explainable ai and its effects on human perceptions"
    - "visual interpretability for deep learning a survey"
    - "what can ai do for me evaluating machine learning interpretations in cooperative play"
    - "interpreting cnn knowledge via an explanatory graph"
    - "interpretable explanations of black boxes by meaningful perturbation"
    - "why should i trust you explaining the predictions of any classifier"

Connected through hyperedge HE-2 (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"
    - "neural network attributions a causal perspective"
    - "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"

Connected through hyperedge HE-3 (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"
    - "on the in fidelity and sensitivity for explanations"
    - "interpreting neural networks using flip points"

Connected through hyperedge HE-4 (induced by source paper "a logic based framework leveraging neural networks for studying the evolution of neurological disorders"):
  with members:
    - "the seventh answer set programming competition design and results"
    - "the external interface for extending wasp"
    - "asp core 2 input language format"
    - "a comprehensive survey on graph neural networks"
    - "constraint answer set programming without grounding"
    - "automated verification of neural networks advances challenges and perspectives"
    - "visual interpretability for deep learning a survey"
    - "formal verification of cnn based perception systems"

Connected through hyperedge HE-5 (induced by source paper "facial age estimation by deep residual decision making"):
  with members:
    - "hierarchical binary cnns for landmark localization with limited resources"
    - "visualizing the decision making process in deep neural decision forest"
    - "interpretable learning for self driving cars by visualizing causal attention"
    - "visualizing the loss landscape of neural nets"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "label distribution learning forests"
    - "interpretable convolutional neural networks"
    - "deep regression forests for age estimation"

Connected through hyperedge HE-6 (induced by source paper "architecting dependable learning enabled autonomous systems a survey"):
  with members:
    - "runtime monitoring neuron activation patterns"
    - "conditional affordance learning for driving in urban environments"
    - "sequence to sequence prediction of vehicle trajectory via lstm encoder decoder architecture"
    - "chauffeurnet learning to drive by imitating the best and synthesizing the worst"
    - "safety and trustworthiness of deep neural networks a survey"
    - "naturalistic driver intention and path prediction using recurrent neural networks"
    - "dynamic occupancy grid prediction for urban autonomous driving a deep learning approach with fully automatic labeling"
    - "provable defenses against adversarial examples via the convex outer adversarial polytope"

Connected through hyperedge HE-7 (induced by source paper "transparent classification with multilayer logical perceptrons and random binarization"):
  with members:
    - "learning algorithms via neural logic networks"
    - "exact and consistent interpretation for piecewise linear neural networks a closed form solution"
    - "interpreting deep visual representations via network dissection"
    - "interpretable convolutional neural networks"
    - "distilling a neural network into a soft decision tree"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "scalable bayesian rule lists"
    - "the mythos of model interpretability"

Connected through hyperedge HE-8 (induced by source paper "leveraging model interpretability and stability to increase model robustness"):
  with members:
    - "adversarial sample detection for deep neural network through model mutation testing"
    - "how important is a neuron"
    - "detecting adversarial samples for deep neural networks through mutation testing"
    - "computationally efficient measures of internal neuron importance"
    - "neural network interpretation via fine grained textual summarization"
    - "axiomatic attribution for deep networks"
    - "a unified approach to interpreting model predictions"
    - "learning important features through propagating activation differences"

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.CV(Computer Vision and Pattern Recognition)