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: "heterogeneous bitwidth binarization in convolutional neural networks"
  Abstract: Recent work has shown that fast, compact low-bitwidth neural networks can be surprisingly accurate. These networks use homogeneous binarization: all parameters in each layer or (more commonly) the whole model have the same low bitwidth (e.g., 2 bits). However, modern hardware allows efficient designs where each arithmetic instruction can have a custom bitwidth, motivating heterogeneous binarization, where every parameter in the network may have a different bitwidth. In this paper, we show that it is feasible and useful to select bitwidths at the parameter granularity during training. For instance a heterogeneously quantized version of modern networks such as AlexNet and MobileNet, with the right mix of 1-, 2- and 3-bit parameters that average to just 1.4 bits can equal the accuracy of homogeneous 2-bit versions of these networks. Further, we provide analyses to show that the heterogeneously binarized systems yield FPGA- and ASIC-based implementations that are correspondingly more efficient in both circuit area and energy efficiency than their homogeneous counterparts.

Connected through hyperedge HE-1 (induced by source paper "similarity preserving knowledge distillation"):
  with members:
    - "lifelong gan continual learning for conditional image generation"
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "cinic 10 is not imagenet or cifar 10"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "knowledge distillation by on the fly native ensemble"
    - "model compression via distillation and quantization"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"

Connected through hyperedge HE-2 (induced by source paper "weight normalization based quantization for deep neural network compression"):
  with members:
    - "regularizing activation distribution for training binarized deep networks"
    - "filter pruning via geometric median for deep convolutional neural networks acceleration"
    - "simultaneously optimizing weight and quantizer of ternary neural network using truncated gaussian approximation"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "synaptic strength for convolutional neural network"
    - "extreme network compression via filter group approximation"
    - "network sketching exploiting binary structure in deep cnns"
    - "deep mutual learning"

Connected through hyperedge HE-3 (induced by source paper "memory driven mixed low precision quantization for enabling deep network inference on microcontrollers"):
  with members:
    - "trained quantization thresholds for accurate and efficient fixed point inference of deep neural networks"
    - "low bit quantization of neural networks for efficient inference"
    - "learning low precision neural networks without straight through estimator ste"
    - "hawq hessian aware quantization of neural networks with mixed precision"
    - "ifq net integrated fixed point quantization networks for embedded vision"
    - "learned step size quantization"
    - "quantizing deep convolutional networks for efficient inference a whitepaper"
    - "fbnet hardware aware efficient convnet design via differentiable neural architecture search"

Connected through hyperedge HE-4 (induced by source paper "semi relaxed quantization with dropbits training low bit neural networks via bit wise regularization"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "differentiable fine grained quantization for deep neural network compression"
    - "relaxed quantization for discretized neural networks"
    - "mixed precision quantization of convnets via differentiable neural architecture search"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "quantization and training of neural networks for efficient integer arithmetic only inference"
    - "learning discrete weights using the local reparameterization trick"
    - "dorefa net training low bitwidth convolutional neural networks with low bitwidth gradients"

Connected through hyperedge HE-5 (induced by source paper "structured binary neural networks for accurate image classification and semantic segmentation"):
  with members:
    - "towards optimal structured cnn pruning via generative adversarial learning"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "model compression via distillation and quantization"
    - "bi real net enhancing the performance of 1 bit cnns with improved representational capability and advanced training algorithm"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "discrimination aware channel pruning for deep neural networks"
    - "learning to train a binary neural network"
    - "syq learning symmetric quantization for efficient deep neural networks"

Connected through hyperedge HE-6 (induced by source paper "automating generation of low precision deep learning operators"):
  with members:
    - "tvm end to end optimization stack for deep learning"
    - "learning to optimize tensor programs"
    - "faster population counts using avx2 instructions"
    - "high performance ultra low precision convolutions on mobile devices"
    - "deep learning with low precision by half wave gaussian quantization"
    - "finn a framework for fast scalable binarized neural network inference"
    - "dorefa net training low bitwidth convolutional neural networks with low bitwidth gradients"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"

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.CV(Computer Vision and Pattern Recognition)