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: "tiny ssd a tiny single shot detection deep convolutional neural network for real time embedded object detection"
  Abstract: Object detection is a major challenge in computer vision, involving both object classification and object localization within a scene. While deep neural networks have been shown in recent years to yield very powerful techniques for tackling the challenge of object detection, one of the biggest challenges with enabling such object detection networks for widespread deployment on embedded devices is high computational and memory requirements. Recently, there has been an increasing focus in exploring small deep neural network architectures for object detection that are more suitable for embedded devices, such as Tiny YOLO and SqueezeDet. Inspired by the efficiency of the Fire microarchitecture introduced in SqueezeNet and the object detection performance of the single-shot detection macroarchitecture introduced in SSD, this paper introduces Tiny SSD, a single-shot detection deep convolutional neural network for real-time embedded object detection that is composed of a highly optimized, non-uniform Fire sub-network stack and a non-uniform sub-network stack of highly optimized SSD-based auxiliary convolutional feature layers designed specifically to minimize model size while maintaining object detection performance. The resulting Tiny SSD possess a model size of 2.3MB (~26X smaller than Tiny YOLO) while still achieving an mAP of 61.3% on VOC 2007 (~4.2% higher than Tiny YOLO). These experimental results show that very small deep neural network architectures can be designed for real-time object detection that are well-suited for embedded scenarios.

Connected through hyperedge HE-1 (induced by source paper "refinedetlite a lightweight one stage object detection framework for cpu only devices"):
  with members:
    - "res2net a new multi scale backbone architecture"
    - "generalized intersection over union a metric and a loss for bounding box regression"
    - "searching for mobilenetv3"
    - "consistent optimization for single shot object detection"
    - "tiny dsod lightweight object detection for resource restricted usages"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "bounding box regression with uncertainty for accurate object detection"
    - "yolov3 an incremental improvement"

Connected through hyperedge HE-2 (induced by source paper "m3d rpn monocular 3d region proposal network for object detection"):
  with members:
    - "pixor real time 3d object detection from point clouds"
    - "a general pipeline for 3d detection of vehicles"
    - "surfconv bridging 3d and 2d convolution for rgbd images"
    - "deep feature pyramid reconfiguration for object detection"
    - "pedestrian detection with autoregressive network phases"
    - "localization recall precision lrp a new performance metric for object detection"
    - "pointrcnn 3d object proposal generation and detection from point cloud"
    - "illuminating pedestrians via simultaneous detection segmentation"

Connected through hyperedge HE-3 (induced by source paper "attonets compact and efficient deep neural networks for the edge via human machine collaborative design"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "nestdnn resource aware multi tenant on device deep learning for continuous mobile vision"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "netscore towards universal metrics for large scale performance analysis of deep neural networks for practical on device edge usage"
    - "monas multi objective neural architecture search using reinforcement learning"
    - "mnasnet platform aware neural architecture search for mobile"
    - "ferminets learning generative machines to generate efficient neural networks via generative synthesis"
    - "quantization and training of neural networks for efficient integer arithmetic only inference"

Connected through hyperedge HE-4 (induced by source paper "deep learning acceleration techniques for real time mobile vision applications"):
  with members:
    - "convolutional neural network for intrusion detection system in cyber physical systems"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "towards high performance video object detection for mobiles"
    - "ai benchmark running deep neural networks on android smartphones"
    - "towards a theoretical understanding of hashing based neural nets"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "deep reinforcement learning for visual object tracking in videos"
    - "an efficient deep learning hashing neural network for mobile visual search"

Connected through hyperedge HE-5 (induced by source paper "edgesegnet a compact network for semantic segmentation"):
  with members:
    - "attonets compact and efficient deep neural networks for the edge via human machine collaborative design"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "netscore towards universal metrics for large scale performance analysis of deep neural networks for practical on device edge usage"
    - "monas multi objective neural architecture search using reinforcement learning"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "mnasnet platform aware neural architecture search for mobile"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"

Connected through hyperedge HE-6 (induced by source paper "fast object detection in compressed jpeg images"):
  with members:
    - "recent advances in object detection in the age of deep convolutional neural networks"
    - "fast object detection in compressed video"
    - "yolov3 an incremental improvement"
    - "deep residual learning in the jpeg transform domain"
    - "harmonic networks integrating spectral information into cnns"
    - "towards image understanding from deep compression without decoding"
    - "fssd feature fusion single shot multibox detector"
    - "compressed video action recognition"

Connected through hyperedge HE-7 (induced by source paper "fsnet compression of deep convolutional neural networks by filter summary"):
  with members:
    - "learning 3d filtermap for deep convolutional neural networks"
    - "thinet a filter level pruning method for deep neural network compression"
    - "ssd single shot multibox detector"
    - "densely connected convolutional networks"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "cyclical learning rates for training neural networks"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

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"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "fast yolo a fast you only look once system for real time embedded object detection in video"
    - "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)