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: "strassennets deep learning with a multiplication budget"
  Abstract: A large fraction of the arithmetic operations required to evaluate deep neural networks (DNNs) are due to matrix multiplications, both in convolutional and fully connected layers. Matrix multiplications can be cast as $2$-layer sum-product networks (SPNs) (arithmetic circuits), disentangling multiplications and additions. We leverage this observation for end-to-end learning of low-cost (in terms of multiplications) approximations of linear operations in DNN layers. Specifically, we propose to replace matrix multiplication operations by SPNs, with widths corresponding to the budget of multiplications we want to allocate to each layer, and learning the edges of the SPNs from data. Experiments on CIFAR-10 and ImageNet show that this method applied to ResNet yields significantly higher accuracy than existing methods for a given multiplication budget, or leads to the same or higher accuracy compared to existing methods while using significantly fewer multiplications. Furthermore, our approach allows fine-grained control of the tradeoff between arithmetic complexity and accuracy of DNN models. Finally, we demonstrate that the proposed framework is able to rediscover Strassen's matrix multiplication algorithm, i.e., it can learn to multiply $2 \times 2$ matrices using only $7$ multiplications instead of $8$.

Connected through hyperedge HE-1 (induced by source paper "searching for winograd aware quantized networks"):
  with members:
    - "a study of bfloat16 for deep learning training"
    - "winograd convolution for dnns beyond linear polynomials"
    - "visual wake words dataset"
    - "sparse sparse architecture search for cnns on resource constrained microcontrollers"
    - "gated scnn gated shape cnns for semantic segmentation"
    - "searching for mobilenetv3"
    - "efficient winograd convolution via integer arithmetic"
    - "differentiable soft quantization bridging full precision and low bit neural networks"

Connected through hyperedge HE-2 (induced by source paper "skynet a hardware efficient method for object detection and tracking on embedded systems"):
  with members:
    - "accuracy vs efficiency achieving both through fpga implementation aware neural architecture search"
    - "got 10k a large high diversity benchmark for generic object tracking in the wild"
    - "skynet a champion model for dac sdc on low power object detection"
    - "single path nas device aware efficient convnet design"
    - "ternary hybrid neural tree networks for highly constrained iot applications"
    - "searching for mobilenetv3"
    - "fcos fully convolutional one stage object detection"
    - "scale aware trident networks for object detection"

Connected through hyperedge HE-3 (induced by source paper "ternary mobilenets via per layer hybrid filter banks"):
  with members:
    - "ternary hybrid neural tree networks for highly constrained iot applications"
    - "compressing rnns for iot devices by 15 38x using kronecker products"
    - "searching for mobilenetv3"
    - "run time efficient rnn compression for inference on edge devices"
    - "pushing the limits of rnn compression"
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "binary ensemble neural network more bits per network or more networks per bit"
    - "amc automl for model compression and acceleration on mobile devices"

Connected through hyperedge HE-4 (induced by source paper "ternary hybrid neural tree networks for highly constrained iot applications"):
  with members:
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "efficient keyword spotting using dilated convolutions and gating"
    - "hello edge keyword spotting on microcontrollers"
    - "towards accurate binary convolutional neural network"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "thinet a filter level pruning method for deep neural network compression"
    - "deep learning with low precision by half wave gaussian quantization"
    - "morphnet fast simple resource constrained structure learning of deep networks"

Connected through hyperedge HE-5 (induced by source paper "learning fast algorithms for linear transforms using butterfly factorizations"):
  with members:
    - "neural arithmetic logic units"
    - "quadrature based features for kernel approximation"
    - "learning compressed transforms with low displacement rank"
    - "c ir cnn accelerating and compressing deep neural networks using block circulant weight matrices"
    - "a semantic loss function for deep learning with symbolic knowledge"
    - "hyperband a novel bandit based approach to hyperparameter optimization"
    - "neural combinatorial optimization with reinforcement learning"
    - "orthogonal random features"

Connected through hyperedge HE-6 (induced by source paper "ecc platform independent energy constrained deep neural network compression via a bilinear regression model"):
  with members:
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "euphrates algorithm soc co design for low power mobile continuous vision"
    - "amc automl for model compression and acceleration on mobile devices"
    - "energy constrained compression for deep neural networks via weighted sparse projection and layer input masking"
    - "a unified framework of dnn weight pruning and weight clustering quantization using admm"
    - "compressing neural networks using the variational information bottleneck"
    - "learning efficient convolutional networks through network slimming"
    - "self normalizing neural networks"

Connected through hyperedge HE-7 (induced by source paper "energy constrained compression for deep neural networks via weighted sparse projection and layer input masking"):
  with members:
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "euphrates algorithm soc co design for low power mobile continuous vision"
    - "amc automl for model compression and acceleration on mobile devices"
    - "apprentice using knowledge distillation techniques to improve low precision network accuracy"
    - "scnn an accelerator for compressed sparse convolutional neural networks"
    - "towards effective low bitwidth convolutional neural networks"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"

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)