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: "focused quantization for sparse cnns"
  Abstract: Deep convolutional neural networks (CNNs) are powerful tools for a wide range of vision tasks, but the enormous amount of memory and compute resources required by CNNs pose a challenge in deploying them on constrained devices. Existing compression techniques, while excelling at reducing model sizes, struggle to be computationally friendly. In this paper, we attend to the statistical properties of sparse CNNs and present focused quantization, a novel quantization strategy based on power-of-two values, which exploits the weight distributions after fine-grained pruning. The proposed method dynamically discovers the most effective numerical representation for weights in layers with varying sparsities, significantly reducing model sizes. Multiplications in quantized CNNs are replaced with much cheaper bit-shift operations for efficient inference. Coupled with lossless encoding, we built a compression pipeline that provides CNNs with high compression ratios (CR), low computation cost and minimal loss in accuracy. In ResNet-50, we achieved a 18.08x CR with only 0.24% loss in top-5 accuracy, outperforming existing compression methods. We fully compressed a ResNet-18 and found that it is not only higher in CR and top-5 accuracy, but also more hardware efficient as it requires fewer logic gates to implement when compared to other state-of-the-art quantization methods assuming the same throughput.

Connected through hyperedge HE-1 (induced by source paper "localization aware channel pruning for object detection"):
  with members:
    - "centernet keypoint triplets for object detection"
    - "generalized intersection over union a metric and a loss for bounding box regression"
    - "mask scoring r cnn"
    - "objects as points"
    - "play and prune adaptive filter pruning for deep model compression"
    - "cornernet detecting objects as paired keypoints"
    - "efficient neural network compression"
    - "discrimination aware channel pruning for deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "cat compression aware training for bandwidth reduction"):
  with members:
    - "ebpc extended bit plane compression for deep neural network inference and training accelerators"
    - "compressibility loss for neural network weights"
    - "feature map transform coding for energy efficient cnn inference"
    - "quantization networks"
    - "lutnet rethinking inference in fpga soft logic"
    - "sparse networks from scratch faster training without losing performance"
    - "differentiable soft quantization bridging full precision and low bit neural networks"
    - "efficient and effective quantization for sparse dnns"

Connected through hyperedge HE-3 (induced by source paper "lutnet learning fpga configurations for highly efficient neural network inference"):
  with members:
    - "lutnet rethinking inference in fpga soft logic"
    - "deep neural network approximation for custom hardware where we ve been where we re going"
    - "toolflows for mapping convolutional neural networks on fpgas a survey and future directions"
    - "in datacenter performance analysis of a tensor processing unit"
    - "towards accurate binary convolutional neural network"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "rebnet residual binarized neural network"
    - "finn a framework for fast scalable binarized neural network inference"

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)