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 "bit efficient quantization for deep neural networks"):
  Abstract: Quantization for deep neural networks have afforded models for edge devices that use less on-board memory and enable efficient low-power inference. In this paper, we present a comparison of model-parameter driven quantization approaches that can achieve as low as 3-bit precision without affecting accuracy. The post-training quantization approaches are data-free, and the resulting weight values are closely tied to the dataset distribution on which the model has converged to optimality. We show quantization results for a number of state-of-art deep neural networks (DNN) using large dataset like ImageNet. To better analyze quantization results, we describe the overall range and local sparsity of values afforded through various quantization schemes. We show the methods to lower bit-precision beyond quantization limits with object class clustering.

Includes members:
  Member "data free quantization through weight equalization and bias correction":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "post training piecewise linear quantization for deep neural networks")
  Member "quantizing deep convolutional networks for efficient inference a whitepaper":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "post training piecewise linear quantization for deep neural networks")
  Member "generalized ternary connect end to end learning and compression of multiplication free deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "toward runtime throttleable neural networks")
  Member "probabilistic binary neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "progressive stochastic binarization of deep networks")
  Member "a survey on methods and theories of quantized neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "shifted and squeezed 8 bit floating point format for low precision training of deep neural networks")
  Member "relaxed quantization for discretized neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "sparse weight activation training")
  Member "quantization and training of neural networks for efficient integer arithmetic only inference":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "searching for winograd aware quantized networks")
  Member "bitnet bit regularized deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "generalized ternary connect end to end learning and compression of multiplication free deep 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)