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: "stealing neural networks via timing side channels"
  Abstract: Deep learning is gaining importance in many applications. However, Neural Networks face several security and privacy threats. This is particularly significant in the scenario where Cloud infrastructures deploy a service with Neural Network model at the back end. Here, an adversary can extract the Neural Network parameters, infer the regularization hyperparameter, identify if a data point was part of the training data, and generate effective transferable adversarial examples to evade classifiers. This paper shows how a Neural Network model is susceptible to timing side channel attack. In this paper, a black box Neural Network extraction attack is proposed by exploiting the timing side channels to infer the depth of the network. Although, constructing an equivalent architecture is a complex search problem, it is shown how Reinforcement Learning with knowledge distillation can effectively reduce the search space to infer a target model. The proposed approach has been tested with VGG architectures on CIFAR10 data set. It is observed that it is possible to reconstruct substitute models with test accuracy close to the target models and the proposed approach is scalable and independent of type of Neural Network architectures.

Connected through hyperedge HE-1 (induced by source paper "how to 0wn nas in your spare time"):
  with members:
    - "the evolved transformer"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "security analysis of deep neural networks operating in the presence of cache side channel attacks"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "why do adversarial attacks transfer explaining transferability of evasion and poisoning attacks"
    - "pseudo lidar from visual depth estimation bridging the gap in 3d object detection for autonomous driving"
    - "mnasnet platform aware neural architecture search for mobile"
    - "learning transferable architectures for scalable image recognition"

Connected through hyperedge HE-2 (induced by source paper "mitigating query flooding parameter duplication attack on regression models with high dimensional gaussian mechanism"):
  with members:
    - "mvg mechanism differential privacy under matrix valued query"
    - "prada protecting against dnn model stealing attacks"
    - "utility optimized local differential privacy mechanisms for distribution estimation"
    - "stealing hyperparameters in machine learning"
    - "model extraction warning in mlaas paradigm"
    - "individual differential privacy a utility preserving formulation of differential privacy guarantees"
    - "practical black box attacks against machine learning"
    - "stealing machine learning models via prediction apis"

Connected through hyperedge HE-3 (induced by source paper "survey of attacks and defenses on edge deployed neural networks"):
  with members:
    - "tensorflow js machine learning for the web and beyond"
    - "bit flip attack crushing neural network with progressive bit search"
    - "xonn xnor based oblivious deep neural network inference"
    - "deeplaser practical fault attack on deep neural networks"
    - "knockoff nets stealing functionality of black box models"
    - "cache telepathy leveraging shared resource attacks to learn dnn architectures"
    - "mlcapsule guarded offline deployment of machine learning as a service"
    - "the secret sharer measuring unintended neural network memorization extracting secrets"

Connected through hyperedge HE-4 (induced by source paper "a framework for the extraction of deep neural networks by leveraging public data"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "neural network model extraction attacks in edge devices by hearing architectural hints"
    - "generative adversarial networks for black box api attacks with limited training data"
    - "adversarial active learning for deep networks a margin based approach"
    - "cache telepathy leveraging shared resource attacks to learn dnn architectures"
    - "copycat cnn stealing knowledge by persuading confession with random non labeled data"
    - "exploring connections between active learning and model extraction"
    - "mlcapsule guarded offline deployment of machine learning as a service"

Connected through hyperedge HE-5 (induced by source paper "open dnn box by power side channel attack"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "finefool fine object contour attack via attention"
    - "i know what you see power side channel attack on convolutional neural network accelerators"
    - "csi neural network using side channels to recover your artificial neural network information"
    - "accelerator aware pruning for convolutional neural networks"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "nisp pruning networks using neuron importance score propagation"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-6 (induced by source paper "quantifying hyper parameter leakage in machine learning"):
  with members:
    - "prediction poisoning towards defenses against dnn model stealing attacks"
    - "a tutorial on learning with bayesian networks"
    - "a framework for the extraction of deep neural networks by leveraging public data"
    - "neural network model extraction attacks in edge devices by hearing architectural hints"
    - "f bleau fast black box leakage estimation"
    - "knockoff nets stealing functionality of black box models"
    - "cache telepathy leveraging shared resource attacks to learn dnn architectures"
    - "exploring connections between active learning and model extraction"

Connected through hyperedge HE-7 (induced by source paper "specusym speculative symbolic execution for cache timing leak detection"):
  with members:
    - "mitigating power side channels during compilation"
    - "specfuzz bringing spectre type vulnerabilities to the surface"
    - "identifying cache based side channels through secret augmented abstract interpretation"
    - "neural network model extraction attacks in edge devices by hearing architectural hints"
    - "abstract interpretation under speculative execution"
    - "ct fuzz fuzzing for timing leaks"
    - "savior towards bug driven hybrid testing"
    - "spoiler speculative load hazards boost rowhammer and cache attacks"

Connected through hyperedge HE-8 (induced by source paper "adversarial frontier stitching for remote neural network watermarking"):
  with members:
    - "tampernn efficient tampering detection of deployed neural nets"
    - "digital watermarking for deep neural networks"
    - "turning your weakness into a strength watermarking deep neural networks by backdooring"
    - "are adversarial examples inevitable"
    - "deepsigns a generic watermarking framework for ip protection of deep learning models"
    - "audio adversarial examples targeted attacks on speech to text"
    - "adversarial perturbations against real time video classification systems"
    - "stealing hyperparameters in machine learning"

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)