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: "adversarial examples detection in deep networks with convolutional filter statistics"
  Abstract: Deep learning has greatly improved visual recognition in recent years. However, recent research has shown that there exist many adversarial examples that can negatively impact the performance of such an architecture. This paper focuses on detecting those adversarial examples by analyzing whether they come from the same distribution as the normal examples. Instead of directly training a deep neural network to detect adversarials, a much simpler approach was proposed based on statistics on outputs from convolutional layers. A cascade classifier was designed to efficiently detect adversarials. Furthermore, trained from one particular adversarial generating mechanism, the resulting classifier can successfully detect adversarials from a completely different mechanism as well. The resulting classifier is non-subdifferentiable, hence creates a difficulty for adversaries to attack by using the gradient of the classifier. After detecting adversarial examples, we show that many of them can be recovered by simply performing a small average filter on the image. Those findings should lead to more insights about the classification mechanisms in deep convolutional neural networks.

Connected through hyperedge HE-1 (induced by source paper "raid randomized adversarial input detection for neural networks"):
  with members:
    - "when explainability meets adversarial learning detecting adversarial examples using shap signatures"
    - "are odds really odd bypassing statistical detection of adversarial examples"
    - "deepsmartfuzzer reward guided test generation for deep learning"
    - "deepfault fault localization for deep neural networks"
    - "a new defense against adversarial images turning a weakness into a strength"
    - "the odds are odd a statistical test for detecting adversarial examples"
    - "on evaluating adversarial robustness"
    - "deepgauge multi granularity testing criteria for deep learning systems"

Connected through hyperedge HE-2 (induced by source paper "erase and restore simple accurate and resilient detection of l 2 adversarial examples"):
  with members:
    - "protecting jpeg images against adversarial attacks"
    - "adversarial robustness toolbox v1 0 0"
    - "deflecting adversarial attacks with pixel deflection"
    - "shield fast practical defense and vaccination for deep learning using jpeg compression"
    - "exploiting the inherent limitation of l0 adversarial examples"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "adversarial examples are not easily detected bypassing ten detection methods"

Connected through hyperedge HE-3 (induced by source paper "detection and recovery of adversarial attacks with injected attractors"):
  with members:
    - "invisible backdoor attacks against deep neural networks"
    - "gotta catch em all using concealed trapdoors to detect adversarial attacks on neural networks"
    - "sitatapatra blocking the transfer of adversarial samples"
    - "hidden trigger backdoor attacks"
    - "feature denoising for improving adversarial robustness"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"

Connected through hyperedge HE-4 (induced by source paper "detecting and recovering adversarial examples an input sensitivity guided method"):
  with members:
    - "simple black box adversarial attacks"
    - "gradient similarity an explainable approach to detect adversarial attacks against deep learning"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "adversarial and clean data are not twins"
    - "magnet a two pronged defense against adversarial examples"
    - "magnet and efficient defenses against adversarial attacks are not robust to adversarial examples"

Connected through hyperedge HE-5 (induced by source paper "unmask adversarial detection and defense through robust feature alignment"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "advertorch v0 1 an adversarial robustness toolbox based on pytorch"
    - "shapeshifter robust physical adversarial attack on faster r cnn object detector"
    - "black box adversarial attacks with limited queries and information"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "magnet a two pronged defense against adversarial examples"
    - "adversary detection in neural networks via persistent homology"
    - "practical black box attacks against machine learning"

Connected through hyperedge HE-6 (induced by source paper "curse of dimensionality on randomized smoothing for certifiable robustness"):
  with members:
    - "certified defenses for adversarial patches"
    - "stochastic activation pruning for robust adversarial defense"
    - "semidefinite relaxations for certifying robustness to adversarial examples"
    - "certified adversarial robustness with additive noise"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "provable defenses against adversarial examples via the convex outer adversarial polytope"
    - "adversarial and clean data are not twins"

Connected through hyperedge HE-7 (induced by source paper "adversarial examples opportunities and challenges"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "atmpa attacking machine learning based malware visualization detection methods via adversarial examples"
    - "hcic hardware assisted control flow integrity checking"
    - "adversarial examples versus cloud based detectors a black box empirical study"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "the malicious use of artificial intelligence forecasting prevention and mitigation"
    - "adversarial attacks against medical deep learning systems"
    - "targeted adversarial examples for black box audio systems"

Connected through hyperedge HE-8 (induced by source paper "semanticadv generating adversarial examples via attribute conditional image editing"):
  with members:
    - "meshadv adversarial meshes for visual recognition"
    - "semantic adversarial attacks parametric transformations that fool deep classifiers"
    - "big but imperceptible adversarial perturbations via semantic manipulation"
    - "spatially transformed adversarial examples"
    - "cosface large margin cosine loss for deep face recognition"
    - "accelerated training for massive classification via dynamic class selection"
    - "large scale gan training for high fidelity natural image synthesis"
    - "attacks meet interpretability attribute steered detection of adversarial samples"

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)