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 "training quantized neural networks with the full precision auxiliary module"):
  Abstract: In this paper, we seek to tackle a challenge in training low-precision networks: the notorious difficulty in propagating gradient through a low-precision network due to the non-differentiable quantization function. We propose a solution by training the low-precision network with a fullprecision auxiliary module. Specifically, during training, we construct a mix-precision network by augmenting the original low-precision network with the full precision auxiliary module. Then the augmented mix-precision network and the low-precision network are jointly optimized. This strategy creates additional full-precision routes to update the parameters of the low-precision model, thus making the gradient back-propagates more easily. At the inference time, we discard the auxiliary module without introducing any computational complexity to the low-precision network. We evaluate the proposed method on image classification and object detection over various quantization approaches and show consistent performance increase. In particular, we achieve near lossless performance to the full-precision model by using a 4-bit detector, which is of great practical value.

Includes members:
  Member "effective training of convolutional neural networks with low bitwidth weights and activations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "generative low bitwidth data free quantization")
  Member "circulant binary convolutional networks enhancing the performance of 1 bit dcnns with circulant back propagation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning architectures for binary networks")
  Member "relational knowledge distillation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "collaborative distillation for ultra resolution universal style transfer")
  Member "bi real net enhancing the performance of 1 bit cnns with improved representational capability and advanced training algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning architectures for binary networks")
  Member "yolov3 an incremental improvement":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "perception and decision making of autonomous systems in the era of learning an overview")
  Member "proxquant quantized neural networks via proximal operators":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "switchable precision neural networks")
  Member "lq nets learned quantization for highly accurate and compact deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "sparse weight activation training")
  Member "self supervised knowledge distillation using singular value decomposition":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "knowledge distillation via adaptive instance normalization")

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)