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 "munet a highly compact deep convolutional neural network architecture for real time embedded traffic sign classification"):
  Abstract: Traffic sign recognition is a very important computer vision task for a number of real-world applications such as intelligent transportation surveillance and analysis. While deep neural networks have been demonstrated in recent years to provide state-of-the-art performance traffic sign recognition, a key challenge for enabling the widespread deployment of deep neural networks for embedded traffic sign recognition is the high computational and memory requirements of such networks. As a consequence, there are significant benefits in investigating compact deep neural network architectures for traffic sign recognition that are better suited for embedded devices. In this paper, we introduce MicronNet, a highly compact deep convolutional neural network for real-time embedded traffic sign recognition designed based on macroarchitecture design principles (e.g., spectral macroarchitecture augmentation, parameter precision optimization, etc.) as well as numerical microarchitecture optimization strategies. The resulting overall architecture of MicronNet is thus designed with as few parameters and computations as possible while maintaining recognition performance, leading to optimized information density of the proposed network. The resulting MicronNet possesses a model size of just ~1MB and ~510,000 parameters (~27x fewer parameters than state-of-the-art) while still achieving a human performance level top-1 accuracy of 98.9% on the German traffic sign recognition benchmark. Furthermore, MicronNet requires just ~10 million multiply-accumulate operations to perform inference, and has a time-to-compute of just 32.19 ms on a Cortex-A53 high efficiency processor. These experimental results show that highly compact, optimized deep neural network architectures can be designed for real-time traffic sign recognition that are well-suited for embedded scenarios.

Includes members:
  Member "netscore towards universal metrics for large scale performance analysis of deep neural networks for practical on device edge usage":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "attonets compact and efficient deep neural networks for the edge via human machine collaborative design")
  Member "towards principled design of deep convolutional networks introducing simpnet":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "hotcake higher order tucker articulated kernels for deeper cnn compression")
  Member "tiny ssd a tiny single shot detection deep convolutional neural network for real time embedded object detection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "refinedetlite a lightweight one stage object detection framework for cpu only devices")
  Member "an analysis of deep neural network models for practical applications":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "crl class representative learning for image classification")
  Member "mobilenets efficient convolutional neural networks for mobile vision applications":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "lossless compression of deep neural networks")
  Member "squishednets squishing squeezenet further for edge device scenarios via deep evolutionary synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "attonets compact and efficient deep neural networks for the edge via human machine collaborative design")
  Member "squeezedet unified small low power fully convolutional neural networks for real time object detection for autonomous driving":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "squeezewave extremely lightweight vocoders for on device speech synthesis")

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.CV(Computer Vision and Pattern Recognition)