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: "embedding deep networks into visual explanations"
  Abstract: In this paper, we propose a novel explanation module to explain the predictions made by a deep network. Explanation module works by embedding a high-dimensional deep network layer nonlinearly into a low-dimensional explanation space while retaining faithfulness, so that the original deep learning predictions can be constructed from the few concepts extracted by the explanation module. We then visualize such concepts for human to learn about the high-level concepts that deep learning is using to make decisions. We propose an algorithm called Sparse Reconstruction Autoencoder (SRAE) for learning the embedding to the explanation space. SRAE aims to reconstruct part of the original feature space while retaining faithfulness. A visualization system is then introduced for human understanding of features in the explanation space. The proposed method is applied to explain CNN models in image classification tasks, and several novel metrics are introduced to evaluate the performance of explanations quantitatively without human involvement. Experiments show that the proposed approach could generate better explanations of the mechanisms CNN use for making predictions.

Connected through hyperedge HE-1 (induced by source paper "counterfactual visual explanations"):
  with members:
    - "grounding visual explanations"
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "explanations based on the missing towards contrastive explanations with pertinent negatives"
    - "teaching categories to human learners with visual explanations"
    - "explaining image classifiers by counterfactual generation"
    - "sanity checks for saliency maps"
    - "visualizing deep neural network decisions prediction difference analysis"
    - "interpretable explanations of black boxes by meaningful perturbation"

Connected through hyperedge HE-2 (induced by source paper "interactive naming for explaining deep neural networks a formative study"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "network dissection quantifying interpretability of deep visual representations"
    - "visual explanations for convolutional neural networks via input resampling"
    - "axiomatic attribution for deep networks"
    - "interpretable explanations of black boxes by meaningful perturbation"
    - "not just a black box learning important features through propagating activation differences"
    - "generating visual explanations"
    - "striving for simplicity the all convolutional net"

Connected through hyperedge HE-3 (induced by source paper "causal learning and explanation of deep neural networks via autoencoded activations"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "theoretical impediments to machine learning with seven sparks from the causal revolution"
    - "relating input concepts to convolutional neural network decisions"
    - "end to end learning for self driving cars"
    - "why should i trust you explaining the predictions of any classifier"
    - "understanding deep image representations by inverting them"
    - "visualizing and understanding convolutional networks"
    - "deep inside convolutional networks visualising image classification models and saliency maps"

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)