human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "pca driven hybrid network design for enabling intelligence at the edge"
  Abstract: The recent advent of IOT has increased the demand for enabling AI-based edge computing in several applications including healthcare monitoring systems, autonomous vehicles etc. This has necessitated the search for efficient implementations of neural networks in terms of both computation and storage. Although extreme quantization has proven to be a powerful tool to achieve significant compression over full-precision networks, it can result in significant degradation in performance for complex image classification tasks. In this work, we propose a Principal Component Analysis (PCA) driven methodology to design mixed-precision, hybrid networks. Unlike standard practices of using PCA for dimensionality reduction, we leverage PCA to identify significant layers in a binary network which contribute relevant transformations on the input data by increasing the number of significant dimensions. Subsequently, we propose Hybrid-Net, a network with increased bit-precision of the weights and activations of the significant layers in a binary network. We show that the proposed Hybrid-Net achieves over 10% improvement in classification accuracy over binary networks such as XNOR-Net for ResNet and VGG architectures on CIFAR-100 and ImageNet datasets while still achieving upto 94% of the energy-efficiency of XNOR-Nets. The proposed design methodology allows us to move closer to the accuracy of standard full-precision networks by keeping more than half of the network binary. This work demonstrates an effective, one-shot methodology for designing hybrid, mixed-precision networks which significantly improve the classification performance of binary networks while attaining remarkable compression. The proposed hybrid networks further the feasibility of using highly compressed neural networks for energy-efficient neural computing in IOT-based edge devices.

Connected through hyperedge HE-1 (induced by source paper "pruning filters while training for efficiently optimizing deep learning networks"):
  with members:
    - "recovering from random pruning on the plasticity of deep convolutional neural networks"
    - "rethinking the value of network pruning"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "dorefa net training low bitwidth convolutional neural networks with low bitwidth gradients"
    - "pruning convolutional neural networks for resource efficient inference"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "pbgen partial binarization of deconvolution based generators for edge intelligence"):
  with members:
    - "efficient hybrid network architectures for extremely quantized neural networks enabling intelligence at the edge"
    - "xnor neural engine a hardware accelerator ip for 21 6 fj op binary neural network inference"
    - "design flow of accelerating hybrid extremely low bit width neural network in embedded fpga"
    - "xnorbin a 95 top s w hardware accelerator for binary convolutional neural networks"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "on the universal approximability and complexity bounds of quantized relu neural networks"
    - "hybrid binary networks optimizing for accuracy efficiency and memory"
    - "binary generative adversarial networks for image retrieval"

Question: Please tell me which class the center node 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.LG(Machine Learning)