human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "amc automl for model compression and acceleration on mobile devices":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "sparsely connected convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "towards robust learning with different label noise distributions")
  Member "sparsely aggregated convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "cspnet a new backbone that can enhance learning capability of cnn")
  Member "shufflenet an extremely efficient convolutional neural network for mobile devices":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "post training piecewise linear quantization for deep neural networks")
  Member "shift a zero flop zero parameter alternative to spatial convolutions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "towards efficient model compression via learned global ranking")
  Member "keynote small neural nets are beautiful enabling embedded systems with small deep neural network architectures":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "resource scalable cnn synthesis for iot applications")
  Member "single image super resolution via a lightweight residual convolutional neural network":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "h owan multi distorted image restoration with tensor 1x1 convolution")
  Member "mobilenets efficient convolutional neural networks for mobile vision applications":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "discrimination aware network pruning for deep model compression")

Question: Please tell me which class the source paper 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)