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 adversarial examples by activation promotion and suppression"
  Abstract: It is widely known that convolutional neural networks (CNNs) are vulnerable to adversarial examples: crafted images with imperceptible perturbations. However, interpretability of these perturbations is less explored in the literature. This work aims to better understand the roles of adversarial perturbations and provide visual explanations from pixel, image and network perspectives. We show that adversaries make a promotion and suppression effect (PSE) on neurons' activation and can be primarily categorized into three types: 1)suppression-dominated perturbations that mainly reduce the classification score of the true label, 2)promotion-dominated perturbations that focus on boosting the confidence of the target label, and 3)balanced perturbations that play a dual role on suppression and promotion. Further, we provide the image-level interpretability of adversarial examples, which links PSE of pixel-level perturbations to class-specific discriminative image regions localized by class activation mapping. Lastly, we analyze the effect of adversarial examples through network dissection, which offers concept-level interpretability of hidden units. We show that there exists a tight connection between the sensitivity (against attacks) of internal response of units with their interpretability on semantic concepts.

Connected through hyperedge HE-1 (induced by source paper "heat and blur an effective and fast defense against adversarial examples"):
  with members:
    - "concise explanations of neural networks using adversarial training"
    - "adversarial examples are a natural consequence of test error in noise"
    - "explaining convolutional neural networks using softmax gradient layer wise relevance propagation"
    - "saliency methods for explaining adversarial attacks"
    - "spatially transformed adversarial examples"
    - "interpretable deep learning under fire"
    - "concise and stable explanations using adversarial training"
    - "feature squeezing detecting adversarial examples in deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "defending against backdoor attack on deep neural networks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "topology attack and defense for graph neural networks an optimization perspective"
    - "req yolo a resource aware efficient quantization framework for object detection on fpgas"
    - "on the design of black box adversarial examples by leveraging gradient free optimization and operator splitting method"
    - "detecting backdoor attacks on deep neural networks by activation clustering"
    - "fine pruning defending against backdooring attacks on deep neural networks"
    - "is feature selection secure against training data poisoning"
    - "spectral signatures in backdoor attacks"

Connected through hyperedge HE-3 (induced by source paper "towards an efficient and general framework of robust training for graph neural networks"):
  with members:
    - "topology attack and defense for graph neural networks an optimization perspective"
    - "on the design of black box adversarial examples by leveraging gradient free optimization and operator splitting method"
    - "zo adamm zeroth order adaptive momentum method for black box optimization"
    - "adversarial attacks on graph neural networks via meta learning"
    - "certifiable robustness and robust training for graph convolutional networks"
    - "adversarial attack on graph structured data"
    - "zeroth order stochastic variance reduction for nonconvex optimization"
    - "universal decision based black box perturbations breaking security through obscurity defenses"

Connected through hyperedge HE-4 (induced by source paper "adversarial robustness vs model compression or both"):
  with members:
    - "topology attack and defense for graph neural networks an optimization perspective"
    - "a direct approach to robust deep learning using adversarial networks"
    - "defensive quantization when efficiency meets robustness"
    - "adv bnn improved adversarial defense through robust bayesian neural network"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "sparse dnns with improved adversarial robustness"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "training for faster adversarial robustness verification via inducing relu stability"

Connected through hyperedge HE-5 (induced by source paper "nattack learning the distributions of adversarial examples for an improved black box attack on deep neural networks"):
  with members:
    - "a direct approach to robust deep learning using adversarial networks"
    - "adv bnn improved adversarial defense through robust bayesian neural network"
    - "stochastic activation pruning for robust adversarial defense"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "lipschitz margin training scalable certification of perturbation invariance for deep neural networks"
    - "how local is the local diversity reinforcing sequential determinantal point processes with dynamic ground sets for supervised video summarization"
    - "on the robustness of the cvpr 2018 white box adversarial example defenses"
    - "deflecting adversarial attacks with pixel deflection"

Connected through hyperedge HE-6 (induced by source paper "interpreting and improving adversarial robustness of deep neural networks with neuron sensitivity"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "simple black box adversarial attacks"
    - "the odds are odd a statistical test for detecting adversarial examples"
    - "adversarial logit pairing"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "network dissection quantifying interpretability of deep visual representations"
    - "defense against adversarial attacks using high level representation guided denoiser"

Connected through hyperedge HE-7 (induced by source paper "on the design of black box adversarial examples by leveraging gradient free optimization and operator splitting method"):
  with members:
    - "topology attack and defense for graph neural networks an optimization perspective"
    - "defensive quantization when efficiency meets robustness"
    - "fault sneaking attack a stealthy framework for misleading deep neural networks"
    - "autozoom autoencoder based zeroth order optimization method for attacking black box neural networks"
    - "defensive dropout for hardening deep neural networks under adversarial attacks"
    - "adversarial generative nets neural network attacks on state of the art face recognition"
    - "exploring the space of black box attacks on deep neural networks"
    - "black box adversarial attacks with limited queries and information"

Connected through hyperedge HE-8 (induced by source paper "interpreting and improving adversarial robustness with neuron sensitivity"):
  with members:
    - "adversarial logit pairing"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "network dissection quantifying interpretability of deep visual representations"
    - "defense against adversarial attacks using high level representation guided denoiser"
    - "safetynet detecting and rejecting adversarial examples robustly"
    - "towards interpretable deep neural networks by leveraging adversarial examples"
    - "countering adversarial images using input transformations"

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)