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 "pbgen partial binarization of deconvolution based generators for edge intelligence"):
  Abstract: This work explores the binarization of the deconvolution-based generator in a GAN for memory saving and speedup of image construction. Our study suggests that different from convolutional neural networks (including the discriminator) where all layers can be binarized, only some of the layers in the generator can be binarized without significant performance loss. Supported by theoretical analysis and verified by experiments, a direct metric based on the dimension of deconvolution operations is established, which can be used to quickly decide which layers in the generator can be binarized. Our results also indicate that both the generator and the discriminator should be binarized simultaneously for balanced competition and better performance. Experimental results based on CelebA suggest that directly applying state-of-the-art binarization techniques to all the layers of the generator will lead to 2.83$\times$ performance loss measured by sliced Wasserstein distance compared with the original generator, while applying them to selected layers only can yield up to 25.81$\times$ saving in memory consumption, and 1.96$\times$ and 1.32$\times$ speedup in inference and training respectively with little performance loss.

Includes members:
  Member "efficient hybrid network architectures for extremely quantized neural networks enabling intelligence at the edge":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "edge intelligence the confluence of edge computing and artificial intelligence")
  Member "pca driven hybrid network design for enabling intelligence at the edge":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "pruning filters while training for efficiently optimizing deep learning networks")
  Member "xnor neural engine a hardware accelerator ip for 21 6 fj op binary neural network inference":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "pulp nn accelerating quantized neural networks on parallel ultra low power risc v processors")
  Member "design flow of accelerating hybrid extremely low bit width neural network in embedded fpga":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "autodnnchip an automated dnn chip predictor and builder for both fpgas and asics")
  Member "xnorbin a 95 top s w hardware accelerator for binary convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "tentaclenet a pseudo ensemble template for accurate binary convolutional neural networks")
  Member "lq nets learned quantization for highly accurate and compact deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "sparse weight activation training")
  Member "on the universal approximability and complexity bounds of quantized relu neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "dlbc a deep learning based consensus in blockchains for deep learning services")
  Member "hybrid binary networks optimizing for accuracy efficiency and memory":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "constructing energy efficient mixed precision neural networks through principal component analysis for edge intelligence")

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)