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: "computationally efficient target classification in multispectral image data with deep neural networks"
  Abstract: Detecting and classifying targets in video streams from surveillance cameras is a cumbersome, error-prone and expensive task. Often, the incurred costs are prohibitive for real-time monitoring. This leads to data being stored locally or transmitted to a central storage site for post-incident examination. The required communication links and archiving of the video data are still expensive and this setup excludes preemptive actions to respond to imminent threats. An effective way to overcome these limitations is to build a smart camera that analyzes the data on-site, close to the sensor, and transmits alerts when relevant video sequences are detected.     Deep neural networks (DNNs) have come to outperform humans in visual classifications tasks and are also performing exceptionally well on other computer vision tasks. The concept of DNNs and Convolutional Networks (ConvNets) can easily be extended to make use of higher-dimensional input data such as multispectral data. We explore this opportunity in terms of achievable accuracy and required computational effort.     To analyze the precision of DNNs for scene labeling in an urban surveillance scenario we have created a dataset with 8 classes obtained in a field experiment. We combine an RGB camera with a 25-channel VIS-NIR snapshot sensor to assess the potential of multispectral image data for target classification. We evaluate several new DNNs, showing that the spectral information fused together with the RGB frames can be used to improve the accuracy of the system or to achieve similar accuracy with a 3x smaller computation effort. We achieve a very high per-pixel accuracy of 99.1%. Even for scarcely occurring, but particularly interesting classes, such as cars, 75% of the pixels are labeled correctly with errors occurring only around the border of the objects. This high accuracy was obtained with a training set of only 30 labeled images, paving the way for fast adaptation to various application scenarios.

Connected through hyperedge HE-1 (induced by source paper "ebpc extended bit plane compression for deep neural network inference and training accelerators"):
  with members:
    - "and the bit goes down revisiting the quantization of neural networks"
    - "additive noise annealing and approximation properties of quantized neural networks"
    - "a 64 mw dnn based visual navigation engine for autonomous nano drones"
    - "rpr random partition relaxation for training binary and ternary weight neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "yolov3 an incremental improvement"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "multiposenet fast multi person pose estimation using pose residual network"

Connected through hyperedge HE-2 (induced by source paper "hyperdrive a multi chip systolically scalable binary weight cnn inference engine"):
  with members:
    - "mixed precision training of convolutional neural networks using integer operations"
    - "xnorbin a 95 top s w hardware accelerator for binary convolutional neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "yolov3 an incremental improvement"
    - "extended bit plane compression for convolutional neural network accelerators"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "in datacenter performance analysis of a tensor processing unit"
    - "cas cnn a deep convolutional neural network for image compression artifact suppression"

Connected through hyperedge HE-3 (induced by source paper "cbinfer exploiting frame to frame locality for faster convolutional network inference on video streams"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "yolov3 an incremental improvement"
    - "tensor comprehensions framework agnostic high performance machine learning abstractions"
    - "t cnn tubelets with convolutional neural networks for object detection from videos"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "a comparison of cnn based face and head detectors for real time video surveillance applications"
    - "incremental network quantization towards lossless cnns with low precision weights"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"

Connected through hyperedge HE-4 (induced by source paper "extended bit plane compression for convolutional neural network accelerators"):
  with members:
    - "dnn feature map compression using learned representation over gf 2"
    - "cbinfer exploiting frame to frame locality for faster convolutional network inference on video streams"
    - "scnn an accelerator for compressed sparse convolutional neural networks"
    - "incremental network quantization towards lossless cnns with low precision weights"
    - "nullhop a flexible convolutional neural network accelerator based on sparse representations of feature maps"
    - "a survey on deep learning in medical image analysis"
    - "compressing dma engine leveraging activation sparsity for training deep neural networks"
    - "origami a 803 gop s w convolutional network accelerator"

Connected through hyperedge HE-5 (induced by source paper "cbinfer change based inference for convolutional neural networks on video data"):
  with members:
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "nullhop a flexible convolutional neural network accelerator based on sparse representations of feature maps"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "origami a 803 gop s w convolutional network accelerator"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "understanding the impact of precision quantization on the accuracy and energy of neural networks"
    - "hardware oriented approximation of convolutional neural networks"
    - "clockwork convnets for video semantic segmentation"

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)