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: "cache telepathy leveraging shared resource attacks to learn dnn architectures"
  Abstract: Deep Neural Networks (DNNs) are fast becoming ubiquitous for their ability to attain good accuracy in various machine learning tasks. A DNN's architecture (i.e., its hyper-parameters) broadly determines the DNN's accuracy and performance, and is often confidential. Attacking a DNN in the cloud to obtain its architecture can potentially provide major commercial value. Further, attaining a DNN's architecture facilitates other, existing DNN attacks. #R##N#This paper presents Cache Telepathy: a fast and accurate mechanism to steal a DNN's architecture using the cache side channel. Our attack is based on the insight that DNN inference relies heavily on tiled GEMM (Generalized Matrix Multiply), and that DNN architecture parameters determine the number of GEMM calls and the dimensions of the matrices used in the GEMM functions. Such information can be leaked through the cache side channel. #R##N#This paper uses Prime+Probe and Flush+Reload to attack VGG and ResNet DNNs running OpenBLAS and Intel MKL libraries. Our attack is effective in helping obtain the architectures by very substantially reducing the search space of target DNN architectures. For example, for VGG using OpenBLAS, it reduces the search space from more than $10^{35}$ architectures to just 16.

Connected through hyperedge HE-1 (induced by source paper "neural network model extraction attacks in edge devices by hearing architectural hints"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "tvm end to end optimization stack for deep learning"
    - "i know what you see power side channel attack on convolutional neural network accelerators"
    - "sok security and privacy in machine learning"
    - "stealing hyperparameters in machine learning"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "learning transferable architectures for scalable image recognition"
    - "benchip benchmarking intelligence processors"

Connected through hyperedge HE-2 (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"
    - "mlcapsule guarded offline deployment of machine learning as a service"
    - "the secret sharer measuring unintended neural network memorization extracting secrets"
    - "security analysis of deep neural networks operating in the presence of cache side channel attacks"

Connected through hyperedge HE-3 (induced by source paper "evaluating differentially private machine learning in practice"):
  with members:
    - "differentially private model publishing for deep learning"
    - "privacy preserving collaborative deep learning with irregular participants"
    - "differentially private empirical risk minimization revisited faster and more general"
    - "dp admm admm based distributed learning with differential privacy"
    - "how to backdoor federated learning"
    - "ml leaks model and data independent membership inference attacks and defenses on machine learning models"
    - "efficient deep learning on multi source private data"
    - "detecting violations of differential privacy"

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"
    - "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"
    - "security analysis of deep neural networks operating in the presence of cache side channel attacks"

Connected through hyperedge HE-5 (induced by source paper "ipguard protecting the intellectual property of deep neural networks via fingerprinting the classification boundary"):
  with members:
    - "neural network model extraction attacks in edge devices by hearing architectural hints"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "deepmarks a digital fingerprinting framework for deep neural networks"
    - "digital watermarking for deep neural networks"
    - "turning your weakness into a strength watermarking deep neural networks by backdooring"
    - "prada protecting against dnn model stealing attacks"
    - "stealing hyperparameters in machine learning"
    - "learning transferable architectures for scalable image recognition"

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"
    - "exploring connections between active learning and model extraction"
    - "security analysis of deep neural networks operating in the presence of cache side channel attacks"

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 "ten ai stepping stones for cybersecurity"):
  with members:
    - "machine learning ddos detection for consumer internet of things devices"
    - "adversarial deep learning for robust detection of binary encoded malware"
    - "provable limitations of deep learning"
    - "adversarial malware binaries evading deep learning for malware detection in executables"
    - "a first look at deep learning apps on smartphones"
    - "prada protecting against dnn model stealing attacks"
    - "logan membership inference attacks against generative models"
    - "malicious url detection using machine learning a survey"

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.DC(Distributed, Parallel, and Cluster Computing)