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 "switchable precision neural networks"):
  Abstract: Instantaneous and on demand accuracy-efficiency trade-off has been recently explored in the context of neural networks slimming. In this paper, we propose a flexible quantization strategy, termed Switchable Precision neural Networks (SP-Nets), to train a shared network capable of operating at multiple quantization levels. At runtime, the network can adjust its precision on the fly according to instant memory, latency, power consumption and accuracy demands. For example, by constraining the network weights to 1-bit with switchable precision activations, our shared network spans from BinaryConnect to Binarized Neural Network, allowing to perform dot-products using only summations or bit operations. In addition, a self-distillation scheme is proposed to increase the performance of the quantized switches. We tested our approach with three different quantizers and demonstrate the performance of SP-Nets against independently trained quantized models in classification accuracy for Tiny ImageNet and ImageNet datasets using ResNet-18 and MobileNet architectures.

Includes members:
  Member "knowledge adaptation for efficient semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "efficient semantic video segmentation with per frame inference")
  Member "adaptative inference cost with convolutional neural mixture models":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "distilled hierarchical neural ensembles with adaptive inference cost")
  Member "universally slimmable networks and improved training techniques":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "mixpath a unified approach for one shot neural architecture search")
  Member "autoslim towards one shot architecture search for channel numbers":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "slimconv reducing channel redundancy in convolutional neural networks by weights flipping")
  Member "be your own teacher improve the performance of convolutional neural networks via self distillation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "scan a scalable neural networks framework towards compact and efficient models")
  Member "once for all train one network and specialize it for efficient deployment on diverse hardware platforms":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a survey of deep learning applications to autonomous vehicle control")
  Member "searching for accurate binary neural architectures":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning architectures for binary networks")
  Member "auxiliary learning for deep multi task learning":
    also connected through other hyperedges:
      (none)

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)