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: "security risks in deep learning implementations"
  Abstract: Advance in deep learning algorithms overshadows their security risk in software implementations. This paper discloses a set of vulnerabilities in popular deep learning frameworks including Caffe, TensorFlow, and Torch. Contrast to the small code size of deep learning models, these deep learning frameworks are complex and contain heavy dependencies on numerous open source packages. This paper considers the risks caused by these vulnerabilities by studying their impact on common deep learning applications such as voice recognition and image classifications. By exploiting these framework implementations, attackers can launch denial-of-service attacks that crash or hang a deep learning application, or control-flow hijacking attacks that cause either system compromise or recognition evasions. The goal of this paper is to draw attention on the software implementations and call for the community effort to improve the security of deep learning frameworks.

Connected through hyperedge HE-1 (induced by source paper "adversarial machine learning industry perspectives"):
  with members:
    - "the deepfake detection challenge dfdc preview dataset"
    - "secml a python library for secure and explainable machine learning"
    - "failure modes in machine learning systems"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "adversarial music real world audio adversary against wake word detection system"
    - "manipulating machine learning poisoning attacks and countermeasures for regression learning"
    - "adversarial generative nets neural network attacks on state of the art face recognition"
    - "adversarial reprogramming of neural networks"

Connected through hyperedge HE-2 (induced by source paper "hacking neural networks a short introduction"):
  with members:
    - "deep leakage from gradients"
    - "iris verification with convolutional neural network and unit circle layer"
    - "adversarial attacks and defences competition"
    - "fine pruning defending against backdooring attacks on deep neural networks"
    - "backdooring convolutional neural networks via targeted weight perturbations"
    - "invisible mask practical attacks on face recognition with infrared"
    - "microsoft malware classification challenge"
    - "a survey of techniques for improving security of gpus"

Connected through hyperedge HE-3 (induced by source paper "adversarial initialization when your network performs the way i want"):
  with members:
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "poison frogs targeted clean label poisoning attacks on neural networks"
    - "wolf in sheep s clothing the downscaling attack against deep learning applications"
    - "adversarial dropout for supervised and semi supervised learning"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "on the robustness of convolutional neural networks to internal architecture and weight perturbations"
    - "stealing machine learning models via prediction apis"
    - "membership inference attacks against machine learning models"

Connected through hyperedge HE-4 (induced by source paper "shredder learning noise distributions to protect inference privacy"):
  with members:
    - "using self supervised learning can improve model robustness and uncertainty"
    - "chiron privacy preserving machine learning as a service"
    - "gazelle a low latency framework for secure neural network inference"
    - "self supervised learning for spinal mris"
    - "rappor randomized aggregatable privacy preserving ordinal response"
    - "how transferable are features in deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "model reuse attacks on deep learning systems"):
  with members:
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "is feature selection secure against training data poisoning"
    - "eagleeye attack agnostic defense against adversarial inputs technical report"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "thinet a filter level pruning method for deep neural network compression"
    - "badnets identifying vulnerabilities in the machine learning model supply chain"
    - "towards poisoning of deep learning algorithms with back gradient optimization"
    - "interpretable explanations of black boxes by meaningful perturbation"

Connected through hyperedge HE-6 (induced by source paper "law and adversarial machine learning"):
  with members:
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "the malicious use of artificial intelligence forecasting prevention and mitigation"
    - "manipulating machine learning poisoning attacks and countermeasures for regression learning"
    - "defense against the dark arts an overview of adversarial example security research and future research directions"
    - "adversarial robustness toolbox v0 3 0"
    - "motivating the rules of the game for adversarial example research"
    - "robust physical world attacks on deep learning models"
    - "badnets identifying vulnerabilities in the machine learning model supply chain"

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)