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: "hu fu hardware and software collaborative attack framework against neural networks"
  Abstract: Recently, Deep Learning (DL), especially Convolutional Neural Network (CNN), develops rapidly and is applied to many tasks, such as image classification, face recognition, image segmentation, and human detection. Due to its superior performance, DL-based models have a wide range of application in many areas, some of which are extremely safety-critical, e.g. intelligent surveillance and autonomous driving. Due to the latency and privacy problem of cloud computing, embedded accelerators are popular in these safety-critical areas. However, the robustness of the embedded DL system might be harmed by inserting hardware/software Trojans into the accelerator and the neural network model, since the accelerator and deploy tool (or neural network model) are usually provided by third-party companies. Fortunately, inserting hardware Trojans can only achieve inflexible attack, which means that hardware Trojans can easily break down the whole system or exchange two outputs, but can't make CNN recognize unknown pictures as targets. Though inserting software Trojans has more freedom of attack, it often requires tampering input images, which is not easy for attackers. So, in this paper, we propose a hardware-software collaborative attack framework to inject hidden neural network Trojans, which works as a back-door without requiring manipulating input images and is flexible for different scenarios. We test our attack framework for image classification and face recognition tasks, and get attack success rate of 92.6% and 100% on CIFAR10 and YouTube Faces, respectively, while keeping almost the same accuracy as the unattacked model in the normal mode. In addition, we show a specific attack scenario in which a face recognition system is attacked and gives a specific wrong answer.

Connected through hyperedge HE-1 (induced by source paper "februus input purification defense against trojan attacks on deep neural network systems"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "strip a defence against trojan attacks on deep neural networks"
    - "tabor a highly accurate approach to inspecting and restoring trojan backdoors in ai systems"
    - "detecting backdoor attacks on deep neural networks by activation clustering"
    - "fine pruning defending against backdooring attacks on deep neural networks"
    - "sentinet detecting physical attacks against deep learning systems"
    - "how to backdoor federated learning"
    - "hardware trojan attacks on neural networks"

Connected through hyperedge HE-2 (induced by source paper "detecting ai trojans using meta neural analysis"):
  with members:
    - "strip a defence against trojan attacks on deep neural networks"
    - "detecting backdoor attacks on deep neural networks by activation clustering"
    - "exploiting unintended feature leakage in collaborative learning"
    - "fine pruning defending against backdooring attacks on deep neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "sentinet detecting physical attacks against deep learning systems"
    - "backdooring convolutional neural networks via targeted weight perturbations"
    - "turning your weakness into a strength watermarking deep neural networks by backdooring"

Connected through hyperedge HE-3 (induced by source paper "regula sub rosa latent backdoor attacks on deep neural networks"):
  with members:
    - "detecting backdoor attacks on deep neural networks by activation clustering"
    - "fine pruning defending against backdooring attacks on deep neural networks"
    - "adversarial patch"
    - "adversarial logit pairing"
    - "manipulating machine learning poisoning attacks and countermeasures for regression learning"
    - "spectral signatures in backdoor attacks"
    - "hardware trojan attacks on neural networks"
    - "feature squeezing detecting adversarial examples in deep neural networks"

Connected through hyperedge HE-4 (induced by source paper "rallying adversarial techniques against deep learning for network security"):
  with members:
    - "adversarial examples are a natural consequence of test error in noise"
    - "on evaluating adversarial robustness"
    - "spatially transformed adversarial examples"
    - "adversarial attacks and defences competition"
    - "adversarial vision challenge"
    - "kitsune an ensemble of autoencoders for online network intrusion detection"
    - "adversarial spheres"
    - "adversarial malware binaries evading deep learning for malware detection in executables"

Connected through hyperedge HE-5 (induced by source paper "tbt targeted neural network attack with bit trojan"):
  with members:
    - "terminal brain damage exposing the graceless degradation in deep neural networks under hardware fault attacks"
    - "bit flip attack crushing neural network with progressive bit search"
    - "detecting backdoor attacks on deep neural networks by activation clustering"
    - "fine pruning defending against backdooring attacks on deep neural networks"
    - "potrojan powerful neural level trojan designs in deep learning models"
    - "hardware trojan attacks on neural networks"
    - "enhancing robustness of machine learning systems via data transformations"
    - "another flip in the wall of rowhammer defenses"

Connected through hyperedge HE-6 (induced by source paper "red attack resource efficient decision based attack for machine learning"):
  with members:
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "detecting backdoor attacks on deep neural networks by activation clustering"
    - "genattack practical black box attacks with gradient free optimization"
    - "potrojan powerful neural level trojan designs in deep learning models"
    - "foolbox v0 8 0 a python toolbox to benchmark the robustness of machine learning models"
    - "badnets identifying vulnerabilities in the machine learning model supply chain"
    - "towards evaluating the robustness of neural networks"
    - "adversarial examples in the physical world"

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.CR(Cryptography and Security)