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 "proximal mean field for neural network quantization"):
  Abstract: Compressing large Neural Networks (NN) by quantizing the parameters, while maintaining the performance is highly desirable due to reduced memory and time complexity. In this work, we cast NN quantization as a discrete labelling problem and leverage results from the extensively studied MRF optimization literature. Specifically, we examine relaxations to the discrete labelling problem, leading to an efficient iterative optimization procedure that involves stochastic gradient descent followed by a projection. We prove that our simple projected gradient descent approach is, in fact, equivalent to a proximal version of the well-known mean-field method. These findings allow the decades-old and theoretically grounded research on MRF optimization to be used to design better network quantization schemes. Our experiments on standard classification datasets (MNIST, CIFAR10/100, TinyImageNet) with convolutional and residual architectures evidence that our algorithm obtains fully-quantized networks with accuracies very close to the floating-point reference networks.

Includes members:
  Member "proxquant quantized neural networks via proximal operators":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "switchable precision neural networks")
  Member "a survey on methods and theories of quantized neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "shifted and squeezed 8 bit floating point format for low precision training of deep neural networks")
  Member "binaryrelax a relaxation approach for training deep neural networks with quantized weights":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "gradient ell_1 regularization for quantization robustness")
  Member "snip single shot network pruning based on connection sensitivity":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "wrpn wide reduced precision networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "least squares binary quantization of neural networks")
  Member "snapshot ensembles train 1 get m for free":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "distilled hierarchical neural ensembles with adaptive inference cost")
  Member "efficient linear programming for dense crfs":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on regularized losses for weakly supervised cnn segmentation")
  Member "from softmax to sparsemax a sparse model of attention and multi label classification":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "power bert accelerating bert inference for classification tasks")

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)