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: "shift based primitives for efficient convolutional neural networks"
  Abstract: We propose a collection of three shift-based primitives for building efficient compact CNN-based networks. These three primitives (channel shift, address shift, shortcut shift) can reduce the inference time on GPU while maintains the prediction accuracy. These shift-based primitives only moves the pointer but avoids memory copy, thus very fast. For example, the channel shift operation is 12.7x faster compared to channel shuffle in ShuffleNet but achieves the same accuracy. The address shift and channel shift can be merged into the point-wise group convolution and invokes only a single kernel call, taking little time to perform spatial convolution and channel shift. Shortcut shift requires no time to realize residual connection through allocating space in advance. We blend these shift-based primitives with point-wise group convolution and built two inference-efficient CNN architectures named AddressNet and Enhanced AddressNet. Experiments on CIFAR100 and ImageNet datasets show that our models are faster and achieve comparable or better accuracy.

Connected through hyperedge HE-1 (induced by source paper "synetgy algorithm hardware co design for convnet accelerators on embedded fpgas"):
  with members:
    - "co design of deep neural nets and neural net accelerators for embedded vision applications"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "squeezesegv2 improved model structure and unsupervised domain adaptation for road object segmentation from a lidar point cloud"
    - "bridging the accuracy gap for 2 bit quantized neural networks qnn"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "finn r an end to end deep learning framework for fast exploration of quantized neural networks"
    - "pact parameterized clipping activation for quantized neural networks"
    - "squeezenext hardware aware neural network design"

Connected through hyperedge HE-2 (induced by source paper "addernet do we really need multiplications in deep learning"):
  with members:
    - "discrimination aware channel pruning for deep neural networks"
    - "signsgd compressed optimisation for non convex problems"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "shift a zero flop zero parameter alternative to spatial convolutions"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "thinet a filter level pruning method for deep neural network compression"
    - "deep learning with low precision by half wave gaussian quantization"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"

Connected through hyperedge HE-3 (induced by source paper "tsm temporal shift module for efficient video understanding"):
  with members:
    - "eco efficient convolutional network for online video understanding"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "amc automl for model compression and acceleration on mobile devices"
    - "motion feature network fixed motion filter for action recognition"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "tvm an automated end to end optimizing compiler for deep learning"
    - "haq hardware aware automated quantization with mixed precision"
    - "videos as space time region graphs"

Connected through hyperedge HE-4 (induced by source paper "structured pruning for efficient convnets via incremental regularization"):
  with members:
    - "amc automl for model compression and acceleration on mobile devices"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "structured probabilistic pruning for convolutional neural network acceleration"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "channel pruning for accelerating very deep neural networks"
    - "learning structured sparsity in deep neural networks"

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)