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: "cbinfer exploiting frame to frame locality for faster convolutional network inference on video streams"
  Abstract: The last few years have brought advances in computer vision at an amazing pace, grounded on new findings in deep neural network construction and training as well as the availability of large labeled datasets. Applying these networks to images demands a high computational effort and pushes the use of state-of-the-art networks on real-time video data out of reach of embedded platforms. Many recent works focus on reducing network complexity for real-time inference on embedded computing platforms. We adopt an orthogonal viewpoint and propose a novel algorithm exploiting the spatio-temporal sparsity of pixel changes. This optimized inference procedure resulted in an average speed-up of 9.1x over cuDNN on the Tegra X2 platform at a negligible accuracy loss of <0.1% and no retraining of the network for a semantic segmentation application. Similarly, an average speed-up of 7.0x has been achieved for a pose detection DNN and a reduction of 5x of the number of arithmetic operations to be performed for object detection on static camera video surveillance data. These throughput gains combined with a lower power consumption result in an energy efficiency of 511 GOp/s/W compared to 70 GOp/s/W for the baseline.

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 "rpr random partition relaxation for training binary and ternary weight neural networks"):
  with members:
    - "ebpc extended bit plane compression for deep neural network inference and training accelerators"
    - "improving neural network quantization without retraining using outlier channel splitting"
    - "quantization networks"
    - "fann on mcu an open source toolkit for energy efficient neural network inference at the edge of the internet of things"
    - "hawq hessian aware quantization of neural networks with mixed precision"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "fbnet hardware aware efficient convnet design via differentiable neural architecture search"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "fann on mcu an open source toolkit for energy efficient neural network inference at the edge of the internet of things"):
  with members:
    - "a 64 mw dnn based visual navigation engine for autonomous nano drones"
    - "deep learning architect classification for architectural design through the eye of artificial intelligence"
    - "cmsis nn efficient neural network kernels for arm cortex m cpus"
    - "towards interpretable deep neural networks by leveraging adversarial examples"
    - "origami a 803 gop s w convolutional network accelerator"
    - "cbinfer change based inference for convolutional neural networks on video data"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "an analysis of visitors behavior in the louvre museum a study using bluetooth data"

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"
    - "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"
    - "eie efficient inference engine on compressed deep neural network"

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)