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: "euphrates algorithm soc co design for low power mobile continuous vision"
  Abstract: Continuous computer vision (CV) tasks increasingly rely on convolutional neural networks (CNN). However, CNNs have massive compute demands that far exceed the performance and energy constraints of mobile devices. In this paper, we propose and develop an algorithm-architecture co-designed system, Euphrates, that simultaneously improves the energy-efficiency and performance of continuous vision tasks. #R##N#Our key observation is that changes in pixel data between consecutive frames represents visual motion. We first propose an algorithm that leverages this motion information to relax the number of expensive CNN inferences required by continuous vision applications. We co-design a mobile System-on-a-Chip (SoC) architecture to maximize the efficiency of the new algorithm. The key to our architectural augmentation is to co-optimize different SoC IP blocks in the vision pipeline collectively. Specifically, we propose to expose the motion data that is naturally generated by the Image Signal Processor (ISP) early in the vision pipeline to the CNN engine. Measurement and synthesis results show that Euphrates achieves up to 66% SoC-level energy savings (4 times for the vision computations), with only 1% accuracy loss.

Connected through hyperedge HE-1 (induced by source paper "balancing efficiency and flexibility for dnn acceleration via temporal gpu systolic array integration"):
  with members:
    - "modeling deep learning accelerator enabled gpus"
    - "in datacenter performance analysis of a tensor processing unit"
    - "mask r cnn"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "learning to track at 100 fps with deep regression networks"
    - "orb slam a versatile and accurate monocular slam system"
    - "cudnn efficient primitives for deep learning"

Connected through hyperedge HE-2 (induced by source paper "ecnn a block based and highly parallel cnn accelerator for edge inference"):
  with members:
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "morph flexible acceleration for 3d cnn based video understanding"
    - "scale sim systolic cnn accelerator simulator"
    - "eva 2 exploiting temporal redundancy in live computer vision"
    - "in datacenter performance analysis of a tensor processing unit"
    - "dslr quality photos on mobile devices with deep convolutional networks"
    - "scnn an accelerator for compressed sparse convolutional neural networks"

Connected through hyperedge HE-3 (induced by source paper "adascale towards real time video object detection using adaptive scaling"):
  with members:
    - "t cnn tubelets with convolutional neural networks for object detection from videos"
    - "eva 2 exploiting temporal redundancy in live computer vision"
    - "towards high performance video object detection"
    - "focal loss for dense object detection"
    - "youtube boundingboxes a large high precision human annotated data set for object detection in video"
    - "deformable convolutional networks"
    - "recurrent scale approximation for object detection in cnn"
    - "detect to track and track to detect"

Connected through hyperedge HE-4 (induced by source paper "asv accelerated stereo vision system"):
  with members:
    - "fixynn efficient hardware for mobile computer vision via transfer learning"
    - "packing sparse convolutional neural networks for efficient systolic array implementations column combining under joint optimization"
    - "dnn dataflow choice is overrated"
    - "morph flexible acceleration for 3d cnn based video understanding"
    - "on the importance of stereo for accurate depth estimation an efficient semi supervised deep neural network approach"
    - "scale sim systolic cnn accelerator simulator"
    - "mobile machine learning hardware at arm a systems on chip soc perspective"
    - "eva 2 exploiting temporal redundancy in live computer vision"

Connected through hyperedge HE-5 (induced by source paper "high throughput cnn inference on embedded arm big little multi core processors"):
  with members:
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "tvm end to end optimization stack for deep learning"
    - "ai benchmark running deep neural networks on android smartphones"
    - "mobile machine learning hardware at arm a systems on chip soc perspective"
    - "scheduling computation graphs of deep learning models on manycore cpus"
    - "optimizing cnn model inference on cpus"
    - "toward low flying autonomous mav trail navigation using deep neural networks for environmental awareness"
    - "modeling the resource requirements of convolutional neural networks on mobile devices"

Connected through hyperedge HE-6 (induced by source paper "fixynn efficient hardware for mobile computer vision via transfer learning"):
  with members:
    - "fix your classifier the marginal value of training the last weight layer"
    - "toolflows for mapping convolutional neural networks on fpgas a survey and future directions"
    - "eva 2 exploiting temporal redundancy in live computer vision"
    - "towards closing the energy gap between hog and cnn features for embedded vision"
    - "scnn an accelerator for compressed sparse convolutional neural networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "learning multiple visual domains with residual adapters"
    - "c ir cnn accelerating and compressing deep neural networks using block circulant weight matrices"

Connected through hyperedge HE-7 (induced by source paper "smaug end to end full stack simulation infrastructure for deep learning workloads"):
  with members:
    - "mlperf inference benchmark"
    - "optimizing gpu cache policies for mi workloads"
    - "asv accelerated stereo vision system"
    - "exploiting parallelism opportunities with deep learning frameworks"
    - "tvm an automated end to end optimizing compiler for deep learning"
    - "deep learning inference in facebook data centers characterization performance optimizations and hardware implications"
    - "eva 2 exploiting temporal redundancy in live computer vision"
    - "in datacenter performance analysis of a tensor processing unit"

Connected through hyperedge HE-8 (induced by source paper "ecc platform independent energy constrained deep neural network compression via a bilinear regression model"):
  with members:
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "amc automl for model compression and acceleration on mobile devices"
    - "energy constrained compression for deep neural networks via weighted sparse projection and layer input masking"
    - "a unified framework of dnn weight pruning and weight clustering quantization using admm"
    - "compressing neural networks using the variational information bottleneck"
    - "learning efficient convolutional networks through network slimming"
    - "self normalizing neural networks"
    - "towards closing the energy gap between hog and cnn features for embedded vision"

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)