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: "a bi directional co design approach to enable deep learning on iot devices"
  Abstract: Developing deep learning models for resource-constrained Internet-of-Things (IoT) devices is challenging, as it is difficult to achieve both good quality of results (QoR), such as DNN model inference accuracy, and quality of service (QoS), such as inference latency, throughput, and power consumption. Existing approaches typically separate the DNN model development step from its deployment on IoT devices, resulting in suboptimal solutions. In this paper, we first introduce a few interesting but counterintuitive observations about such a separate design approach, and empirically show why it may lead to suboptimal designs. Motivated by these observations, we then propose a novel and practical bi-directional co-design approach: a bottom-up DNN model design strategy together with a top-down flow for DNN accelerator design. It enables a joint optimization of both DNN models and their deployment configurations on IoT devices as represented as FPGAs. We demonstrate the effectiveness of the proposed co-design approach on a real-life object detection application using Pynq-Z1 embedded FPGA. Our method obtains the state-of-the-art results on both QoR with high accuracy (IoU) and QoS with high throughput (FPS) and high energy efficiency.

Connected through hyperedge HE-1 (induced by source paper "skynet a champion model for dac sdc on low power object detection"):
  with members:
    - "dac sdc low power object detection challenge for uav applications"
    - "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"

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)