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 "deep learning acceleration techniques for real time mobile vision applications"):
  Abstract: Deep Learning (DL) has become a crucial technology for Artificial Intelligence (AI). It is a powerful technique to automatically extract high-level features from complex data which can be exploited for applications such as computer vision, natural language processing, cybersecurity, communications, and so on. For the particular case of computer vision, several algorithms like object detection in real time videos have been proposed and they work well on Desktop GPUs and distributed computing platforms. However these algorithms are still heavy for mobile and embedded visual applications. The rapid spreading of smart portable devices and the emerging 5G network are introducing new smart multimedia applications in mobile environments. As a consequence, the possibility of implementing deep neural networks to mobile environments has attracted a lot of researchers. This paper presents emerging deep learning acceleration techniques that can enable the delivery of real time visual recognition into the hands of end users, anytime and anywhere.

Includes members:
  Member "convolutional neural network for intrusion detection system in cyber physical systems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "1d cnn based network intrusion detection with normalization on imbalanced data")
  Member "mobilenetv2 inverted residuals and linear bottlenecks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "towards high performance video object detection for mobiles":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "joint anchor feature refinement for real time accurate object detection in images and videos")
  Member "ai benchmark running deep neural networks on android smartphones":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "replacing mobile camera isp with a single deep learning model")
  Member "towards a theoretical understanding of hashing based neural nets":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a comparative study of neural network 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-6 (induced by source paper "refinedetlite a lightweight one stage object detection framework for cpu only devices")
  Member "mobilenets efficient convolutional neural networks for mobile vision applications":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "deep reinforcement learning for visual object tracking in videos":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deep reinforcement learning for active human pose estimation")

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)