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 "skynet a hardware efficient method for object detection and tracking on embedded systems"):
  Abstract: Developing object detection and tracking on resource-constrained embedded systems is challenging. While object detection is one of the most compute-intensive tasks from the artificial intelligence domain, it is only allowed to use limited computation and memory resources on embedded devices. In the meanwhile, such resource-constrained implementations are often required to satisfy additional demanding requirements such as real-time response, high-throughput performance, and reliable inference accuracy. To overcome these challenges, we propose SkyNet, a hardware-efficient method to deliver the state-of-the-art detection accuracy and speed for embedded systems. Instead of following the common top-down flow for compact DNN design, SkyNet provides a bottom-up DNN design approach with comprehensive understanding of the hardware constraints at the very beginning to deliver hardware-efficient DNNs. The effectiveness of SkyNet is demonstrated by winning the extremely competitive System Design Contest for low power object detection in the 56th IEEE/ACM Design Automation Conference (DAC-SDC), where our SkyNet significantly outperforms all other 100+ competitors: it delivers 0.731 Intersection over Union (IoU) and 67.33 frames per second (FPS) on a TX2 embedded GPU; and 0.716 IoU and 25.05 FPS on an Ultra96 embedded FPGA. The evaluation of SkyNet is also extended to GOT-10K, a recent large-scale high-diversity benchmark for generic object tracking in the wild. For state-of-the-art object trackers SiamRPN++ and SiamMask, where ResNet-50 is employed as the backbone, implementations using our SkyNet as the backbone DNN are 1.60X and 1.73X faster with better or similar accuracy when running on a 1080Ti GPU, and 37.20X smaller in terms of parameter size for significantly better memory and storage footprint.

Includes members:
  Member "accuracy vs efficiency achieving both through fpga implementation aware neural architecture search":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "nass optimizing secure inference via neural architecture search")
  Member "got 10k a large high diversity benchmark for generic object tracking in the wild":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "siamese box adaptive network for visual tracking")
  Member "skynet a champion model for dac sdc on low power object detection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "dnn chip predictor an analytical performance predictor for dnn accelerators with various dataflows and hardware architectures")
  Member "single path nas device aware efficient convnet design":
    also connected through other hyperedges:
      (none)
  Member "ternary hybrid neural tree networks for highly constrained iot applications":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "ternary mobilenets via per layer hybrid filter banks")
  Member "searching for mobilenetv3":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "gan compression efficient architectures for interactive conditional gans")
  Member "fcos fully convolutional one stage object detection":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "dsgn deep stereo geometry network for 3d object detection")
  Member "scale aware trident networks for object detection":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "revisiting the sibling head in object detector")

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)