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: "theoretical properties for neural networks with weight matrices of low displacement rank"
  Abstract: Recently low displacement rank (LDR) matrices, or so-called structured matrices, have been proposed to compress large-scale neural networks. Empirical results have shown that neural networks with weight matrices of LDR matrices, referred as LDR neural networks, can achieve significant reduction in space and computational complexity while retaining high accuracy. We formally study LDR matrices in deep learning. First, we prove the universal approximation property of LDR neural networks with a mild condition on the displacement operators. We then show that the error bounds of LDR neural networks are as efficient as general neural networks with both single-layer and multiple-layer structure. Finally, we propose back-propagation based training algorithm for general LDR neural networks.

Connected through hyperedge HE-1 (induced by source paper "req yolo a resource aware efficient quantization framework for object detection on fpgas"):
  with members:
    - "yolov3 an incremental improvement"
    - "c lstm enabling efficient lstm using structured compression techniques on fpgas"
    - "batch renormalization towards reducing minibatch dependence in batch normalized models"
    - "c ir cnn accelerating and compressing deep neural networks using block circulant weight matrices"
    - "finn a framework for fast scalable binarized neural network inference"
    - "ese efficient speech recognition engine with sparse lstm on fpga"
    - "a unified multi scale deep convolutional neural network for fast object detection"
    - "learning structured sparsity in deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "circconv a structured convolution with low complexity"):
  with members:
    - "c ir cnn accelerating and compressing deep neural networks using block circulant weight matrices"
    - "learning structured sparsity in deep neural networks"
    - "wide residual networks"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "acdc a structured efficient linear layer"
    - "deep residual learning for image recognition"
    - "an exploration of parameter redundancy in deep networks with circulant projections"
    - "rethinking the inception architecture for computer vision"

Connected through hyperedge HE-3 (induced by source paper "on the universal approximability and complexity bounds of quantized relu neural networks"):
  with members:
    - "design flow of accelerating hybrid extremely low bit width neural network in embedded fpga"
    - "amc automl for model compression and acceleration on mobile devices"
    - "syq learning symmetric quantization for efficient deep neural networks"
    - "attention is all you need"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "extremely low bit neural network squeeze the last bit out with admm"
    - "training quantized nets a deeper understanding"
    - "neural network with unbounded activation functions is universal approximator"

Connected through hyperedge HE-4 (induced by source paper "adam admm a unified systematic framework of structured weight pruning for dnns"):
  with members:
    - "a systematic dnn weight pruning framework using alternating direction method of multipliers"
    - "in datacenter performance analysis of a tensor processing unit"
    - "exploring the regularity of sparse structure in convolutional neural networks"
    - "c ir cnn accelerating and compressing deep neural networks using block circulant weight matrices"
    - "learning to prune deep neural networks via layer wise optimal brain surgeon"
    - "channel pruning for accelerating very deep neural networks"
    - "nest a neural network synthesis tool based on a grow and prune paradigm"
    - "learning structured sparsity in deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "a unified framework of dnn weight pruning and weight clustering quantization using admm"):
  with members:
    - "progressive weight pruning of deep neural networks using admm"
    - "a systematic dnn weight pruning framework using alternating direction method of multipliers"
    - "adam admm a unified systematic framework of structured weight pruning for dnns"
    - "extremely low bit neural network squeeze the last bit out with admm"
    - "incremental network quantization towards lossless cnns with low precision weights"
    - "nest a neural network synthesis tool based on a grow and prune paradigm"
    - "dorefa net training low bitwidth convolutional neural networks with low bitwidth gradients"
    - "net trim convex pruning of deep neural networks with performance guarantee"

Connected through hyperedge HE-6 (induced by source paper "e rnn design optimization for efficient recurrent neural networks in fpgas"):
  with members:
    - "a systematic dnn weight pruning framework using alternating direction method of multipliers"
    - "c ir cnn accelerating and compressing deep neural networks using block circulant weight matrices"
    - "ese efficient speech recognition engine with sparse lstm on fpga"
    - "maximizing cnn accelerator efficiency through resource partitioning"
    - "eie efficient inference engine on compressed deep neural network"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "fixed point quantization of deep convolutional networks"
    - "convolutional neural networks with low rank regularization"

Connected through hyperedge HE-7 (induced by source paper "on the universal approximation property and equivalence of stochastic computing based neural networks and binary neural networks"):
  with members:
    - "deep reinforcement learning framework applications and embedded implementations"
    - "finn a framework for fast scalable binarized neural network inference"
    - "hardware driven nonlinear activation for stochastic computing based deep convolutional neural networks"
    - "benefits of depth in neural networks"
    - "why deep neural networks for function approximation"
    - "eie efficient inference engine on compressed deep neural network"
    - "quantized neural networks training neural networks with low precision weights and activations"

Connected through hyperedge HE-8 (induced by source paper "c lstm enabling efficient lstm using structured compression techniques on fpgas"):
  with members:
    - "towards ultra high performance and energy efficiency of deep learning systems an algorithm hardware co optimization framework"
    - "c ir cnn accelerating and compressing deep neural networks using block circulant weight matrices"
    - "ese efficient speech recognition engine with sparse lstm on fpga"
    - "convolutional networks for fast energy efficient neuromorphic computing"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "an exploration of parameter redundancy in deep networks with circulant projections"

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)