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: "structured deep neural network pruning via matrix pivoting"
  Abstract: Deep Neural Networks (DNNs) are the key to the state-of-the-art machine vision, sensor fusion and audio/video signal processing. Unfortunately, their computation complexity and tight resource constraints on the Edge make them hard to leverage on mobile, embedded and IoT devices. Due to great diversity of Edge devices, DNN designers have to take into account the hardware platform and application requirements during network training. In this work we introduce pruning via matrix pivoting as a way to improve network pruning by compromising between the design flexibility of architecture-oblivious and performance efficiency of architecture-aware pruning, the two dominant techniques for obtaining resource-efficient DNNs. We also describe local and global network optimization techniques for efficient implementation of the resulting pruned networks. In combination, the proposed pruning and implementation result in close to linear speed up with the reduction of network coefficients during pruning.

Connected through hyperedge HE-1 (induced by source paper "tuning algorithms and generators for efficient edge inference"):
  with members:
    - "synetgy algorithm hardware co design for convnet accelerators on embedded fpgas"
    - "fully learnable group convolution for acceleration of deep neural networks"
    - "bridging the accuracy gap for 2 bit quantized neural networks qnn"
    - "learning grouped convolution for efficient domain adaptation"
    - "mpdcompress matrix permutation decomposition algorithm for deep neural network compression"
    - "chamnet towards efficient network design through platform aware model adaptation"
    - "block sparse recurrent neural networks"
    - "in datacenter performance analysis of a tensor processing unit"

Connected through hyperedge HE-2 (induced by source paper "mpdcompress matrix permutation decomposition algorithm for deep neural network compression"):
  with members:
    - "shakeout a new approach to regularized deep neural network training"
    - "on the importance of stereo for accurate depth estimation an efficient semi supervised deep neural network approach"
    - "toward low flying autonomous mav trail navigation using deep neural networks for environmental awareness"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "deeppicar a low cost deep neural network based autonomous car"
    - "sep nets small and effective pattern networks"
    - "ese efficient speech recognition engine with sparse lstm on fpga"

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)