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: "expanding a robot s life low power object recognition via fpga based dcnn deployment"
  Abstract: FPGAs are commonly used to accelerate domain-specific algorithmic implementations, as they can achieve impressive performance boosts, are reprogrammable and exhibit minimal power consumption. In this work, the SqueezeNet DCNN is accelerated using an SoC FPGA in order for the offered object recognition resource to be employed in a robotic application. Experiments are conducted to investigate the performance and power consumption of the implementation in comparison to deployment on other widely-used computational systems.

Connected through hyperedge HE-1 (induced by source paper "pca rect an energy efficient object detection approach for event cameras"):
  with members:
    - "yolov3 an incremental improvement"
    - "hats histograms of averaged time surfaces for robust event based object classification"
    - "dart distribution aware retinal transform for event based cameras"
    - "phased lstm accelerating recurrent network training for long or event based sequences"
    - "training deep spiking neural networks using backpropagation"
    - "hfirst a temporal approach to object recognition"
    - "converting static image datasets to spiking neuromorphic datasets using saccades"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-2 (induced by source paper "software defined fpga accelerator design for mobile deep learning applications"):
  with members:
    - "squeezejet high level synthesis accelerator design for deep convolutional neural networks"
    - "keynote small neural nets are beautiful enabling embedded systems with small deep neural network architectures"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "striving for simplicity the all convolutional net"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-3 (induced by source paper "a framework of transfer learning in object detection for embedded systems"):
  with members:
    - "using convolutional neural networks in robots with limited computational resources detecting nao robots while playing soccer"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "mask r cnn"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "overcoming catastrophic forgetting in neural networks"
    - "yolo9000 better faster stronger"
    - "speed accuracy trade offs for modern convolutional object detectors"

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)