human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "colorectal cancer diagnosis from histology images a comparative study"):
  with members:
    - "convolutional neural networks for breast cancer screening transfer learning with exponential decay"
    - "deep residual learning for image recognition"
    - "rethinking the inception architecture for computer vision"
    - "how transferable are features in deep neural networks"
    - "going deeper with convolutions"

Connected through hyperedge HE-2 (induced by source paper "convolutional neural networks for aerial multi label pedestrian detection"):
  with members:
    - "okutama action an aerial view video dataset for concurrent human action detection"
    - "is faster r cnn doing well for pedestrian detection"
    - "ssd single shot multibox detector"
    - "you only look once unified real time object detection"
    - "vqa visual question answering"
    - "fast r cnn"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-3 (induced by source paper "autonomous braking system via deep reinforcement learning"):
  with members:
    - "an empirical evaluation of deep learning on highway driving"
    - "deepdriving learning affordance for direct perception in autonomous driving"

Connected through hyperedge HE-4 (induced by source paper "reduced memory region based deep convolutional neural network detection"):
  with members:
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "eie efficient inference engine on compressed deep neural network"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "tensorizing neural networks"
    - "learning both weights and connections for efficient neural networks"
    - "quantized convolutional neural networks for mobile devices"
    - "acdc a structured efficient linear layer"
    - "deep residual learning for image recognition"

Question: Please tell me which class the center node 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)