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 convolutional neural networks for pedestrian detection"):
  Abstract: Pedestrian detection is a popular research topic due to its paramount importance for a number of applications, especially in the fields of automotive, surveillance and robotics. Despite the significant improvements, pedestrian detection is still an open challenge that calls for more and more accurate algorithms. In the last few years, deep learning and in particular Convolutional Neural Networks emerged as the state of the art in terms of accuracy for a number of computer vision tasks such as image classification, object detection and segmentation, often outperforming the previous gold standards by a large margin. In this paper, we propose a pedestrian detection system based on deep learning, adapting a general-purpose convolutional network to the task at hand. By thoroughly analyzing and optimizing each step of the detection pipeline we propose an architecture that outperforms traditional methods, achieving a task accuracy close to that of state-of-the-art approaches, while requiring a low computational time. Finally, we tested the system on an NVIDIA Jetson TK1, a 192-core platform that is envisioned to be a forerunner computational brain of future self-driving cars. HighlightsA deep convolutional neural network for pedestrian detection is proposed.The proposed method improves the accuracy performance with respect to handcrafted features.Our method detects pedestrians with a good accuracy and has a reasonable computational complexity.

Includes members:
  Member "filtered channel features for pedestrian detection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "adapted center and scale prediction more stable and more accurate")
  Member "convolutional channel features":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "fmt fusing multi task convolutional neural network for person search")
  Member "taking a deeper look at pedestrians":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "pedestrian detection the elephant in the room")
  Member "going deeper with convolutions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "caffe convolutional architecture for fast feature embedding":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "object detection in optical remote sensing images a survey and a new benchmark")
  Member "ten years of pedestrian detection what have we learned":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "virtual to real adaptation of pedestrian detectors for smart cities")
  Member "pedestrian detection with spatially pooled features and structured ensemble learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "part level convolutional neural networks for pedestrian detection using saliency and boundary box alignment")
  Member "pedestrian detection aided by deep learning semantic tasks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "triplet online instance matching loss for person re identification")

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)