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: "skynet a champion model for dac sdc on low power object detection"
  Abstract: Developing artificial intelligence (AI) at the edge is always challenging, since edge devices have limited computation capability and memory resources but need to meet demanding requirements, such as real-time processing, high throughput performance, and high inference accuracy. To overcome these challenges, we propose SkyNet, an extremely lightweight DNN with 12 convolutional (Conv) layers and only 1.82 megabyte (MB) of parameters following a bottom-up DNN design approach. SkyNet is demonstrated in the 56th IEEE/ACM Design Automation Conference System Design Contest (DAC-SDC), a low power object detection challenge in images captured by unmanned aerial vehicles (UAVs). SkyNet won the first place award for both the GPU and FPGA tracks of the contest: we deliver 0.731 Intersection over Union (IoU) and 67.33 frames per second (FPS) on a TX2 GPU and deliver 0.716 IoU and 25.05 FPS on an Ultra96 FPGA.

Connected through hyperedge HE-1 (induced by source paper "dnn chip predictor an analytical performance predictor for dnn accelerators with various dataflows and hardware architectures"):
  with members:
    - "e2 train training state of the art cnns with over 80 energy savings"
    - "dual dynamic inference enabling more efficient adaptive and controllable deep inference"
    - "dnn dataflow choice is overrated"
    - "scale sim systolic cnn accelerator simulator"
    - "deep k means re training and parameter sharing with harder cluster assignments for compressing deep convolutions"
    - "eyeriss v2 a flexible accelerator for emerging deep neural networks on mobile devices"
    - "eie efficient inference engine on compressed deep neural network"
    - "the microsoft 2016 conversational speech recognition system"

Connected through hyperedge HE-2 (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"
    - "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"
    - "resource constrained neural network architecture search"

Connected through hyperedge HE-3 (induced by source paper "nais neural architecture and implementation search and its applications in autonomous driving"):
  with members:
    - "co design of deep neural nets and neural net accelerators for embedded vision applications"
    - "frustrated with replicating claims of a shared model a solution"
    - "yolov3 an incremental improvement"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "hardware aware machine learning modeling and optimization"
    - "searching toward pareto optimal device aware neural architectures"
    - "learning transferable architectures for scalable image recognition"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"

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)