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: "neural network model extraction attacks in edge devices by hearing architectural hints"
  Abstract: As neural networks continue their reach into nearly every aspect of software operations, the details of those networks become an increasingly sensitive subject. Even those that deploy neural networks embedded in physical devices may wish to keep the inner working of their designs hidden -- either to protect their intellectual property or as a form of protection from adversarial inputs. The specific problem we address is how, through heavy system stack, given noisy and imperfect memory traces, one might reconstruct the neural network architecture including the set of layers employed, their connectivity, and their respective dimension sizes. Considering both the intra-layer architecture features and the inter-layer temporal association information introduced by the DNN design empirical experience, we draw upon ideas from speech recognition to solve this problem. We show that off-chip memory address traces and PCIe events provide ample information to reconstruct such neural network architectures accurately. We are the first to propose such accurate model extraction techniques and demonstrate an end-to-end attack experimentally in the context of an off-the-shelf Nvidia GPU platform with full system stack. Results show that the proposed techniques achieve a high reverse engineering accuracy and improve the one's ability to conduct targeted adversarial attack with success rate from 14.6\%$\sim$25.5\% (without network architecture knowledge) to 75.9\% (with extracted network architecture).

Connected through hyperedge HE-1 (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"
    - "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"
    - "security analysis of deep neural networks operating in the presence of cache side channel attacks"

Connected through hyperedge HE-2 (induced by source paper "ipguard protecting the intellectual property of deep neural networks via fingerprinting the classification boundary"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "deepmarks a digital fingerprinting framework for deep neural networks"
    - "cache telepathy leveraging shared resource attacks to learn dnn architectures"
    - "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-3 (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"
    - "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"
    - "security analysis of deep neural networks operating in the presence of cache side channel attacks"

Connected through hyperedge HE-4 (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"
    - "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"
    - "kleespectre detecting information leakage through speculative cache attacks via symbolic execution"

Connected through hyperedge HE-5 (induced by source paper "stealing neural networks via timing side channels"):
  with members:
    - "simple physical adversarial examples against end to end autonomous driving models"
    - "updates leak data set inference and reconstruction attacks in online learning"
    - "xonn xnor based oblivious deep neural network inference"
    - "knockoff nets stealing functionality of black box models"
    - "cache telepathy leveraging shared resource attacks to learn dnn architectures"
    - "copycat cnn stealing knowledge by persuading confession with random non labeled data"
    - "textbugger generating adversarial text against real world applications"
    - "mlcapsule guarded offline deployment of machine learning as a service"

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)