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: "dac sdc low power object detection challenge for uav applications"
  Abstract: The 55th Design Automation Conference (DAC) held its first System Design Contest (SDC) in 2018. SDC'18 features a lower power object detection challenge (LPODC) on designing and implementing novel algorithms based object detection in images taken from unmanned aerial vehicles (UAV). The dataset includes 95 categories and 150k images, and the hardware platforms include Nvidia's TX2 and Xilinx's PYNQ Z1. DAC-SDC'18 attracted more than 110 entries from 12 countries. This paper presents in detail the dataset and evaluation procedure. It further discusses the methods developed by some of the entries as well as representative results. The paper concludes with directions for future improvements.

Connected through hyperedge HE-1 (induced by source paper "skynet a hardware efficient method for object detection and tracking on embedded systems"):
  with members:
    - "accuracy vs efficiency achieving both through fpga implementation aware neural architecture search"
    - "got 10k a large high diversity benchmark for generic object tracking in the wild"
    - "skynet a champion model for dac sdc on low power object detection"
    - "single path nas device aware efficient convnet design"
    - "ternary hybrid neural tree networks for highly constrained iot applications"
    - "searching for mobilenetv3"
    - "fcos fully convolutional one stage object detection"
    - "scale aware trident networks for object detection"

Connected through hyperedge HE-2 (induced by source paper "scnn a general distribution based statistical convolutional neural network with application to video object detection"):
  with members:
    - "yolov3 an incremental improvement"
    - "quantization of fully convolutional networks for accurate biomedical image segmentation"
    - "t cnn tubelets with convolutional neural networks for object detection from videos"
    - "flow guided feature aggregation for video object detection"
    - "densely connected convolutional networks"
    - "seq nms for video object detection"
    - "unitbox an advanced object detection network"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "fpga dnn co design an efficient design methodology for iot intelligence on the edge"):
  with members:
    - "design flow of accelerating hybrid extremely low bit width neural network in embedded fpga"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "regularized evolution for image classifier architecture search"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "mnasnet platform aware neural architecture search for mobile"
    - "learning transferable architectures for scalable image recognition"
    - "ese efficient speech recognition engine with sparse lstm on fpga"
    - "neural architecture search with reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "skynet a champion model for dac sdc on low power object detection"):
  with members:
    - "a bi directional co design approach to enable deep learning on iot devices"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "ssd single shot multibox detector"
    - "you only look once unified real time object detection"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-5 (induced by source paper "on the quantization of cellular neural networks for cyber physical systems"):
  with members:
    - "incremental network quantization towards lossless cnns with low precision weights"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "quantized convolutional neural networks for mobile devices"

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)