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: "fast yolo a fast you only look once system for real time embedded object detection in video"
  Abstract: Object detection is considered one of the most challenging problems in this field of computer vision, as it involves the combination of object classification and object localization within a scene. Recently, deep neural networks (DNNs) have been demonstrated to achieve superior object detection performance compared to other approaches, with YOLOv2 (an improved You Only Look Once model) being one of the state-of-the-art in DNN-based object detection methods in terms of both speed and accuracy. Although YOLOv2 can achieve real-time performance on a powerful GPU, it still remains very challenging for leveraging this approach for real-time object detection in video on embedded computing devices with limited computational power and limited memory. In this paper, we propose a new framework called Fast YOLO, a fast You Only Look Once framework which accelerates YOLOv2 to be able to perform object detection in video on embedded devices in a real-time manner. First, we leverage the evolutionary deep intelligence framework to evolve the YOLOv2 network architecture and produce an optimized architecture (referred to as O-YOLOv2 here) that has 2.8X fewer parameters with just a ~2% IOU drop. To further reduce power consumption on embedded devices while maintaining performance, a motion-adaptive inference method is introduced into the proposed Fast YOLO framework to reduce the frequency of deep inference with O-YOLOv2 based on temporal motion characteristics. Experimental results show that the proposed Fast YOLO framework can reduce the number of deep inferences by an average of 38.13%, and an average speedup of ~3.3X for objection detection in video compared to the original YOLOv2, leading Fast YOLO to run an average of ~18FPS on a Nvidia Jetson TX1 embedded system.

Connected through hyperedge HE-1 (induced by source paper "daedalus breaking non maximum suppression in object detection via adversarial examples"):
  with members:
    - "semantic adversarial examples"
    - "model reuse attacks on deep learning systems"
    - "stochastic activation pruning for robust adversarial defense"
    - "adversarial patch"
    - "deep learning for generic object detection a survey"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "yolov3 an incremental improvement"
    - "multi task learning as multi objective optimization"

Connected through hyperedge HE-2 (induced by source paper "pack and detect fast object detection in videos using region of interest packing"):
  with members:
    - "yolov3 an incremental improvement"
    - "recovering from random pruning on the plasticity of deep convolutional neural networks"
    - "recurrent residual module for fast inference in videos"
    - "object detection in videos by short and long range object linking"
    - "t cnn tubelets with convolutional neural networks for object detection from videos"
    - "dynamic deep neural networks optimizing accuracy efficiency trade offs by selective execution"
    - "towards high performance video object detection"
    - "flow guided feature aggregation for video object detection"

Connected through hyperedge HE-3 (induced by source paper "yolo nano a highly compact you only look once convolutional neural network for object detection"):
  with members:
    - "attonets compact and efficient deep neural networks for the edge via human machine collaborative design"
    - "fairnas rethinking evaluation fairness of weight sharing neural architecture search"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "yolov3 an incremental improvement"
    - "netscore towards universal metrics for large scale performance analysis of deep neural networks for practical on device edge usage"
    - "mnasnet platform aware neural architecture search for mobile"
    - "ferminets learning generative machines to generate efficient neural networks via generative synthesis"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-4 (induced by source paper "real time vision based depth reconstruction with nvidia jetson"):
  with members:
    - "sparse 3d point cloud map upsampling and noise removal as a vslam post processing step experimental evaluation"
    - "megadepth learning single view depth prediction from internet photos"
    - "high quality monocular depth estimation via transfer learning"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "cnn slam real time dense monocular slam with learned depth prediction"
    - "learning depth from single monocular images using deep convolutional neural fields"
    - "unsupervised monocular depth estimation with left right consistency"
    - "unsupervised cnn for single view depth estimation geometry to the rescue"

Connected through hyperedge HE-5 (induced by source paper "foveated image processing for faster object detection and recognition in embedded systems using deep convolutional neural networks"):
  with members:
    - "yolov3 an incremental improvement"
    - "learning to zoom a saliency based sampling layer for neural networks"
    - "object detection through search with a foveated visual system"
    - "squeezedet unified small low power fully convolutional neural networks for real time object detection for autonomous driving"
    - "you only look once unified real time object detection"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-6 (induced by source paper "multi cue vehicle detection for semantic video compression in georegistered aerial videos"):
  with members:
    - "xview objects in context in overhead imagery"
    - "yolov3 an incremental improvement"
    - "modnet moving object detection network with motion and appearance for autonomous driving"
    - "ssd single shot multibox detector"
    - "you only look once unified real time object detection"
    - "fast r cnn"

Connected through hyperedge HE-7 (induced by source paper "autofocus efficient multi scale inference"):
  with members:
    - "cornernet detecting objects as paired keypoints"
    - "optimizing video object detection via a scale time lattice"
    - "beyond trade off accelerate fcn based face detector with higher accuracy"
    - "deformable convnets v2 more deformable better results"
    - "path aggregation network for instance segmentation"
    - "sniper efficient multi scale training"
    - "soft nms improving object detection with one line of code"
    - "dynamic deep neural networks optimizing accuracy efficiency trade offs by selective execution"

Connected through hyperedge HE-8 (induced by source paper "towards high performance video object detection for mobiles"):
  with members:
    - "t cnn tubelets with convolutional neural networks for object detection from videos"
    - "tiny ssd a tiny single shot detection deep convolutional neural network for real time embedded object detection"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "towards high performance video object detection"
    - "flow guided feature aggregation for video object detection"
    - "focal loss for dense object detection"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "deformable convolutional networks"

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)