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 change based inference for convolutional neural networks on video data"
  Abstract: Extracting per-frame features using convolutional neural networks for real-time processing of video data is currently mainly performed on powerful GPU-accelerated workstations and compute clusters. However, there are many applications such as smart surveillance cameras that require or would benefit from on-site processing. To this end, we propose and evaluate a novel algorithm for change-based evaluation of CNNs for video data recorded with a static camera setting, exploiting the spatio-temporal sparsity of pixel changes. We achieve an average speed-up of 8.6x over a cuDNN baseline on a realistic benchmark with a negligible accuracy loss of less than 0.1% and no retraining of the network. The resulting energy efficiency is 10x higher than that of per-frame evaluation and reaches an equivalent of 328 GOp/s/W on the Tegra X1 platform.

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"
    - "cbinfer exploiting frame to frame locality for faster convolutional network inference on video streams"
    - "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"
    - "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 "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-5 (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-6 (induced by source paper "deepcache principled cache for mobile deep vision"):
  with members:
    - "eva 2 exploiting temporal redundancy in live computer vision"
    - "practical processing of mobile sensor data for continual deep learning predictions"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "deepiot compressing deep neural network structures for sensing systems with a compressor critic framework"
    - "deepsense a unified deep learning framework for time series mobile sensing data processing"
    - "end to end learning for self driving cars"
    - "densely connected convolutional networks"

Connected through hyperedge HE-7 (induced by source paper "design automation for binarized neural networks a quantum leap opportunity"):
  with members:
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "origami a 803 gop s w convolutional network accelerator"
    - "finn a framework for fast scalable binarized neural network inference"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "reducing the energy cost of inference via in sensor information processing"
    - "analysis and design of a passive switched capacitor matrix multiplier for approximate computing"
    - "deep learning with energy efficient binary gradient cameras"

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)