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 "adabits neural network quantization with adaptive bit widths"):
  Abstract: Deep neural networks with adaptive configurations have gained increasing attention due to the instant and flexible deployment of these models on platforms with different resource budgets. In this paper, we investigate a novel option to achieve this goal by enabling adaptive bit-widths of weights and activations in the model. We first examine the benefits and challenges of training quantized model with adaptive bit-widths, and then experiment with several approaches including direct adaptation, progressive training and joint training. We discover that joint training is able to produce comparable performance on the adaptive model as individual models. We further propose a new technique named Switchable Clipping Level (S-CL) to further improve quantized models at the lowest bit-width. With our proposed techniques applied on a bunch of models including MobileNet-V1/V2 and ResNet-50, we demonstrate that bit-width of weights and activations is a new option for adaptively executable deep neural networks, offering a distinct opportunity for improved accuracy-efficiency trade-off as well as instant adaptation according to the platform constraints in real-world applications.

Includes members:
  Member "a survey of deep learning techniques for autonomous driving":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on the road with 16 neurons mental imagery with bio inspired deep neural networks")
  Member "autogan neural architecture search for generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "neural architecture search for deep image prior")
  Member "single path nas designing hardware efficient convnets in less than 4 hours":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "towards efficient model compression via learned global ranking")
  Member "detnas backbone search for object detection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "nas count counting by density with neural architecture search")
  Member "differentiable quantization of deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "bitpruning learning bitlengths for aggressive and accurate quantization")
  Member "auto deeplab hierarchical neural architecture search for semantic image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "automl zero evolving machine learning algorithms from scratch")
  Member "autoqb automl for network quantization and binarization on mobile devices":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning architectures for binary networks")
  Member "once for all train one network and specialize it for efficient deployment on diverse hardware platforms":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "switchable precision neural networks")

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)