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: "shallow networks for high accuracy road object detection"
  Abstract: The ability to automatically detect other vehicles on the road is vital to the safety of partially-autonomous and fully-autonomous vehicles. Most of the high-accuracy techniques for this task are based on R-CNN or one of its faster variants. In the research community, much emphasis has been applied to using 3D vision or complex R-CNN variants to achieve higher accuracy. However, are there more straightforward modifications that could deliver higher accuracy? Yes. We show that increasing input image resolution (i.e. upsampling) offers up to 12 percentage-points higher accuracy compared to an off-the-shelf baseline. We also find situations where earlier/shallower layers of CNN provide higher accuracy than later/deeper layers. We further show that shallow models and upsampled images yield competitive accuracy. Our findings contrast with the current trend towards deeper and larger models to achieve high accuracy in domain specific detection tasks.

Connected through hyperedge HE-1 (induced by source paper "tiny dsod lightweight object detection for resource restricted usages"):
  with members:
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "dssd deconvolutional single shot detector"
    - "dsod learning deeply supervised object detectors from scratch"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "mask r cnn"
    - "a unified multi scale deep convolutional neural network for fast object detection"
    - "yolo9000 better faster stronger"
    - "speed accuracy trade offs for modern convolutional object detectors"

Connected through hyperedge HE-2 (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"
    - "keynote small neural nets are beautiful enabling embedded systems with small deep neural network architectures"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"):
  with members:
    - "systematic evaluation of convolution neural network advances on the imagenet"
    - "distributed deep learning using synchronous stochastic gradient descent"
    - "ristretto hardware oriented approximation of convolutional neural networks"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "eie efficient inference engine on compressed deep neural network"
    - "dsd dense sparse dense training for deep neural networks"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "learning both weights and connections for efficient neural networks"

Connected through hyperedge HE-4 (induced by source paper "residual squeeze vgg16"):
  with members:
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "places an image database for deep scene understanding"
    - "densely connected convolutional networks"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "training deeper convolutional networks with deep supervision"
    - "going deeper with convolutions"

Connected through hyperedge HE-5 (induced by source paper "abnormality detection and localization in chest x rays using deep convolutional neural networks"):
  with members:
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"
    - "a survey on deep learning in medical image analysis"
    - "learning to read chest x rays recurrent neural cascade model for automated image annotation"
    - "deep residual learning for image recognition"
    - "learning deep features for discriminative localization"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"
    - "striving for simplicity the all convolutional net"

Connected through hyperedge HE-6 (induced by source paper "exploring the design space of deep convolutional neural networks at large scale"):
  with members:
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "origami a 803 gop s w convolutional network accelerator"
    - "systematic evaluation of convolution neural network advances on the imagenet"
    - "theano mpi a theano based distributed training framework"
    - "distributed deep learning using synchronous stochastic gradient descent"
    - "ristretto hardware oriented approximation of convolutional neural networks"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "how to scale distributed deep learning"

Connected through hyperedge HE-7 (induced by source paper "squeezedet unified small low power fully convolutional neural networks for real time object detection for autonomous driving"):
  with members:
    - "eie efficient inference engine on compressed deep neural network"
    - "you only look once unified real time 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)