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: "squeezejet high level synthesis accelerator design for deep convolutional neural networks"
  Abstract: Deep convolutional neural networks have dominated the pattern recognition scene by providing much more accurate solutions in computer vision problems such as object recognition and object detection. Most of these solutions come at a huge computational cost, requiring billions of multiply-accumulate operations and, thus, making their use quite challenging in real-time applications that run on embedded mobile (resource-power constrained) hardware. This work presents the architecture, the high-level synthesis design, and the implementation of SqueezeJet, an FPGA accelerator for the inference phase of the SqueezeNet DCNN architecture, which is designed specifically for use in embedded systems. Results show that SqueezeJet can achieve 15.16 times speed-up compared to the software implementation of SqueezeNet running on an embedded mobile processor with less than 1% drop in top-5 accuracy.

Connected through hyperedge HE-1 (induced by source paper "software defined fpga accelerator design for mobile deep learning applications"):
  with members:
    - "expanding a robot s life low power object recognition via fpga based dcnn deployment"
    - "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-2 (induced by source paper "neura ghe exploiting cpu fpga synergies for efficient and flexible cnn inference acceleration on zynq socs"):
  with members:
    - "neurostream scalable and energy efficient deep learning with smart memory cubes"
    - "in datacenter performance analysis of a tensor processing unit"
    - "an iot endpoint system on chip for secure and energy efficient near sensor analytics"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "origami a 803 gop s w convolutional network accelerator"
    - "finn a framework for fast scalable binarized neural network inference"
    - "dialog based language learning"
    - "binaryconnect training deep neural networks with binary weights during propagations"

Connected through hyperedge HE-3 (induced by source paper "expanding a robot s life low power object recognition via fpga based dcnn deployment"):
  with members:
    - "creating an extrovert robotic assistant via iot networking devices"
    - "hardware oriented approximation of convolutional neural networks"
    - "squeezedet unified small low power fully convolutional neural networks for real time object detection for autonomous driving"

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)