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 "a stealthy hardware trojan exploiting the architectural vulnerability of deep learning architectures input interception attack iia"):
  Abstract: Deep learning architectures (DLA) have shown impressive performance in computer vision, natural language processing and so on. Many DLA make use of cloud computing to achieve classification due to the high computation and memory requirements. Privacy and latency concerns resulting from cloud computing has inspired the deployment of DLA on embedded hardware accelerators. To achieve short time-to-market and have access to global experts, state-of-the-art techniques of DLA deployment on hardware accelerators are outsourced to untrusted third parties. This outsourcing raises security concerns as hardware Trojans can be inserted into the hardware design of the mapped DLA of the hardware accelerator. We argue that existing hardware Trojan attacks highlighted in literature have no qualitative means how definite they are of the triggering of the Trojan. Also, most inserted Trojans show a obvious spike in the number of hardware resources utilized on the accelerator at the time of triggering the Trojan or when the payload is active. #R##N#In this paper, we propose a hardware Trojan attack called Input Interception Attack (IIA). In this attack we make use of the statistical properties of layer-by-layer output to make sure that asides from being stealthy, our IIA is able to trigger with some measure of definiteness. This IIA attack is tested on DLA used to classify MNIST and Cifar-10 data sets. The attacked design utilizes approximately up to 2% more LUTs respectively compared to the un-compromised designs. This paper also discusses potential defensive mechanisms that could be used to combat such hardware Trojans based attack in hardware accelerators for DLA.

Includes members:
  Member "detecting sybil attacks using proofs of work and location in vanets":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "performance analysis of mobile cellular connected drones under practical antenna configurations")
  Member "privacy preserving smart parking system using blockchain and private information retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "performance analysis of mobile cellular connected drones under practical antenna configurations")
  Member "b ride ride sharing with privacy preservation trust and fair payment atop public blockchain":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "performance analysis of mobile cellular connected drones under practical antenna configurations")
  Member "privacy preserving and collusion resistant charging coordination schemes for smart grid":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "performance analysis of mobile cellular connected drones under practical antenna configurations")
  Member "blockchain based firmware update scheme tailored for autonomous vehicles":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "optimizing joint probabilistic caching and channel access for clustered d2d networks")
  Member "potrojan powerful neural level trojan designs in deep learning models":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "backdoor attacks against transfer learning with pre trained deep learning models")
  Member "blockchain based charging coordination mechanism for smart grid energy storage units":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "performance analysis of mobile cellular connected drones under practical antenna configurations")
  Member "hardware trojan attacks on neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "sentinet detecting localized universal attacks against deep learning systems")

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)