human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "quantifying hyper parameter leakage in machine learning"):
  Abstract: Machine Learning models, extensively used for various multimedia applications, are offered to users as a blackbox service on the Cloud on a pay-per-query basis. Such blackbox models are commercially valuable to adversaries, making them vulnerable to extraction attacks to reverse engineer the proprietary model thereby violating the model privacy and Intellectual Property. Here, the adversary first extracts the model architecture or hyperparameters through side channel leakage, followed by stealing the functionality of the target model by training the reconstructed architecture on a synthetic dataset. While the attacks proposed in literature are empirical, there is a need for a theoretical framework to measure the information leaked under such extraction attacks. To this extent, in this work, we propose a novel probabilistic framework, Airavata, to estimate the information leakage in such model extraction attacks. This framework captures the fact that extracting the exact target model is difficult due to experimental uncertainty while inferring model hyperparameters and stochastic nature of training to steal the target model functionality. Specifically, we use Bayesian Networks to capture uncertainty in estimating the target model under various extraction attacks based on the subjective notion of probability. We validate the proposed framework under different adversary assumptions commonly adopted in literature to reason about the attack efficacy. This provides a practical tool to infer actionable details about extracting blackbox models and help identify the best attack combination which maximises the knowledge extracted (or information leaked) from the target model.

Includes members:
  Member "prediction poisoning towards defenses against dnn model stealing attacks":
    also connected through other hyperedges:
      (none)
  Member "a tutorial on learning with bayesian networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a content based deep intrusion detection system")
  Member "a framework for the extraction of deep neural networks by leveraging public data":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "entangled watermarks as a defense against model extraction")
  Member "neural network model extraction attacks in edge devices by hearing architectural hints":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a framework for the extraction of deep neural networks by leveraging public data")
  Member "f bleau fast black box leakage estimation":
    also connected through other hyperedges:
      (none)
  Member "knockoff nets stealing functionality of black box models":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "dynamic backdoor attacks against machine learning models")
  Member "cache telepathy leveraging shared resource attacks to learn dnn architectures":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "neural network model extraction attacks in edge devices by hearing architectural hints")
  Member "exploring connections between active learning and model extraction":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "cryptanalytic extraction of neural network models")

Question: Please tell me which class the source paper 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)