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: "keynote small neural nets are beautiful enabling embedded systems with small deep neural network architectures"
  Abstract: Over the last five years Deep Neural Nets have offered more accurate solutions to many problems in speech recognition, and computer vision, and these solutions have surpassed a threshold of acceptability for many applications. As a result, Deep Neural Networks have supplanted other approaches to solving problems in these areas, and enabled many new applications. While the design of Deep Neural Nets is still something of an art form, in our work we have found basic principles of design space exploration used to develop embedded microprocessor architectures to be highly applicable to the design of Deep Neural Net architectures. In particular, we have used these design principles to create a novel Deep Neural Net called SqueezeNet that requires as little as 480KB of storage for its model parameters. We have further integrated all these experiences to develop something of a playbook for creating small Deep Neural Nets for embedded systems.

Connected through hyperedge HE-1 (induced by source paper "resource scalable cnn synthesis for iot applications"):
  with members:
    - "focal loss for dense object detection"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "cappuccino efficient inference software synthesis for mobile system on chips"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "learning both weights and connections for efficient neural networks"
    - "imagenet large scale visual recognition challenge"

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"
    - "expanding a robot s life low power object recognition via fpga based dcnn deployment"
    - "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 "shift based primitives for efficient convolutional neural networks"):
  with members:
    - "amc automl for model compression and acceleration on mobile devices"
    - "sparsely connected convolutional networks"
    - "sparsely aggregated convolutional networks"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "shift a zero flop zero parameter alternative to spatial convolutions"
    - "single image super resolution via a lightweight residual convolutional neural network"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "thinet a filter level pruning method for deep neural network compression"

Connected through hyperedge HE-4 (induced by source paper "slidenet fast and accurate slide quality assessment based on deep neural networks"):
  with members:
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "tv gan generative adversarial network based thermal to visible face recognition"
    - "understanding the mechanisms of deep transfer learning for medical images"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "deep residual learning for image recognition"
    - "segmental hog new descriptor for glomerulus detection in kidney microscopy image"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "co design of deep neural nets and neural net accelerators for embedded vision applications"):
  with members:
    - "in datacenter performance analysis of a tensor processing unit"
    - "scnn an accelerator for compressed sparse convolutional neural networks"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "eie efficient inference engine on compressed deep neural network"
    - "squeezedet unified small low power fully convolutional neural networks for real time object detection for autonomous driving"
    - "shallow networks for high accuracy road object detection"

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)