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 "when single event upset meets deep neural networks observations explorations and remedies"):
  Abstract: Deep Neural Network has proved its potential in various perception tasks and hence become an appealing option for interpretation and data processing in security sensitive systems. However, security-sensitive systems demand not only high perception performance, but also design robustness under various circumstances. Unlike prior works that study network robustness from software level, we investigate from hardware perspective about the impact of Single Event Upset (SEU) induced parameter perturbation (SIPP) on neural networks. We systematically define the fault models of SEU and then provide the definition of sensitivity to SIPP as the robustness measure for the network. We are then able to analytically explore the weakness of a network and summarize the key findings for the impact of SIPP on different types of bits in a floating point parameter, layer-wise robustness within the same network and impact of network depth. Based on those findings, we propose two remedy solutions to protect DNNs from SIPPs, which can mitigate accuracy degradation from 28% to 0.27% for ResNet with merely 0.24-bit SRAM area overhead per parameter.

Includes members:
  Member "accuracy vs efficiency achieving both through fpga implementation aware neural architecture search":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "nass optimizing secure inference via neural architecture search")
  Member "achieving super linear speedup across multi fpga for real time dnn inference":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "co exploration of neural architectures and heterogeneous asic accelerator designs targeting multiple tasks")
  Member "learning phase competition for traffic signal control":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a survey on traffic signal control methods")
  Member "intrinsic image transformation via scale space decomposition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "shadingnet image intrinsics by fine grained shading decomposition")
  Member "neural color transfer between images":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "mask guided portrait editing with conditional gans")
  Member "on the robustness of convolutional neural networks to internal architecture and weight perturbations":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "paradox in deep neural networks similar yet different while different yet similar")
  Member "deep residual learning for image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "very deep convolutional networks for large scale image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "discrimination aware network pruning for deep model compression")

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.LG(Machine Learning)