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: "eva 2 exploiting temporal redundancy in live computer vision"
  Abstract: Hardware support for deep convolutional neural networks (CNNs) is critical to advanced computer vision in mobile and embedded devices. Current designs, however, accelerate generic CNNs; they do not exploit the unique characteristics of real-time vision. We propose to use the temporal redundancy in natural video to avoid unnecessary computation on most frames. A new algorithm, activation motion compensation, detects changes in the visual input and incrementally updates a previously-computed output. The technique takes inspiration from video compression and applies well-known motion estimation techniques to adapt to visual changes. We use an adaptive key frame rate to control the trade-off between efficiency and vision quality as the input changes. We implement the technique in hardware as an extension to existing state-of-the-art CNN accelerator designs. The new unit reduces the average energy per frame by 54.2%, 61.7%, and 87.6% for three CNNs with less than 1% loss in vision accuracy.

Connected through hyperedge HE-1 (induced by source paper "non structured dnn weight pruning is it beneficial in any platform"):
  with members:
    - "synetgy algorithm hardware co design for convnet accelerators on embedded fpgas"
    - "adversarial robustness vs model compression or both"
    - "hypar towards hybrid parallelism for deep learning accelerator array"
    - "2pfpce two phase filter pruning based on conditional entropy"
    - "bag of tricks for image classification with convolutional neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "morph flexible acceleration for 3d cnn based video understanding"

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"
    - "euphrates algorithm soc co design for low power mobile continuous vision"
    - "scale sim systolic cnn accelerator simulator"
    - "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:
    - "euphrates algorithm soc co design for low power mobile continuous vision"
    - "t cnn tubelets with convolutional neural networks for object detection from videos"
    - "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"
    - "euphrates algorithm soc co design for low power mobile continuous vision"
    - "scale sim systolic cnn accelerator simulator"
    - "mobile machine learning hardware at arm a systems on chip soc perspective"

Connected through hyperedge HE-5 (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"
    - "euphrates algorithm soc co design for low power mobile continuous vision"
    - "toolflows for mapping convolutional neural networks on fpgas a survey and future directions"
    - "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-6 (induced by source paper "merit tensor transform for memory efficient vision processing on parallel architectures"):
  with members:
    - "in datacenter performance analysis of a tensor processing unit"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "deformable convolutional networks"
    - "a survey of neuromorphic computing and neural networks in hardware"
    - "flexpoint an adaptive numerical format for efficient training of deep neural networks"
    - "mask r cnn"
    - "real time single image and video super resolution using an efficient sub pixel convolutional neural network"
    - "eie efficient inference engine on compressed deep neural network"

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"
    - "euphrates algorithm soc co design for low power mobile continuous vision"
    - "tvm an automated end to end optimizing compiler for deep learning"
    - "deep learning inference in facebook data centers characterization performance optimizations and hardware implications"
    - "in datacenter performance analysis of a tensor processing unit"

Connected through hyperedge HE-8 (induced by source paper "secs efficient deep stream processing via class skew dichotomy"):
  with members:
    - "ucnn exploiting computational reuse in deep neural networks via weight repetition"
    - "core50 a new dataset and benchmark for continuous object recognition"
    - "branchynet fast inference via early exiting from deep neural networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "lcnn lookup based convolutional neural network"
    - "densely connected convolutional networks"
    - "unsupervised learning of visual representations by solving jigsaw puzzles"

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)