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: "visual explanations for convolutional neural networks via input resampling"
  Abstract: The predictive power of neural networks often costs model interpretability. Several techniques have been developed for explaining model outputs in terms of input features; however, it is difficult to translate such interpretations into actionable insight. Here, we propose a framework to analyze predictions in terms of the model's internal features by inspecting information flow through the network. Given a trained network and a test image, we select neurons by two metrics, both measured over a set of images created by perturbations to the input image: (1) magnitude of the correlation between the neuron activation and the network output and (2) precision of the neuron activation. We show that the former metric selects neurons that exert large influence over the network output while the latter metric selects neurons that activate on generalizable features. By comparing the sets of neurons selected by these two metrics, our framework suggests a way to investigate the internal attention mechanisms of convolutional neural networks.

Connected through hyperedge HE-1 (induced by source paper "interpretable cnns for object classification"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "one pixel attack for fooling deep neural networks"
    - "visual interpretability for deep learning a survey"
    - "explaining neural networks semantically and quantitatively"
    - "network dissection quantifying interpretability of deep visual representations"
    - "interactively transferring cnn patterns for part localization"
    - "explaining the unexplained a class enhanced attentive response clear approach to understanding deep neural networks"
    - "interpreting cnn knowledge via an explanatory graph"

Connected through hyperedge HE-2 (induced by source paper "unsupervised learning of neural networks to explain neural networks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "interpreting cnns via decision trees"
    - "visual interpretability for deep learning a survey"
    - "network dissection quantifying interpretability of deep visual representations"
    - "towards interpretable r cnn by unfolding latent structures"
    - "explaining the unexplained a class enhanced attentive response clear approach to understanding deep neural networks"
    - "interpreting cnn knowledge via an explanatory graph"
    - "dynamic routing between capsules"

Connected through hyperedge HE-3 (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"
    - "embedding deep networks into visual explanations"
    - "network dissection quantifying interpretability of deep visual representations"
    - "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-4 (induced by source paper "interpretable convolutional neural networks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "visual interpretability for deep learning a survey"
    - "network dissection quantifying interpretability of deep visual representations"
    - "explaining the unexplained a class enhanced attentive response clear approach to understanding deep neural networks"
    - "interpreting cnn knowledge via an explanatory graph"
    - "dynamic routing between capsules"
    - "mining object parts from cnns via active question answering"
    - "right for the right reasons training differentiable models by constraining their explanations"

Connected through hyperedge HE-5 (induced by source paper "grad cam improved visual explanations for deep convolutional networks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "tell me where to look guided attention inference network"
    - "contextual explanation networks"
    - "interpretable learning for self driving cars by visualizing causal attention"
    - "excitation backprop for rnns"
    - "learning important features through propagating activation differences"
    - "paying more attention to attention improving the performance of convolutional neural networks via attention transfer"
    - "deep model compression distilling knowledge from noisy teachers"

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)