human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "separable convolutional eigen filters scef building efficient cnns using redundancy analysis"):
  Abstract: The high model complexity of deep learning algorithms enables remarkable learning capacity in many application domains. However, a large number of trainable parameters comes with a high cost. For example, during both the training and inference phases, the numerous trainable parameters consume a large amount of resources, such as CPU/GPU cores, memory and electric power. In addition, from a theoretical statistical learning perspective, the high complexity of the network can result in a high variance in its generalization performance. One way to reduce the complexity of a network without sacrificing its accuracy is to define and identify redundancies in order to remove them. In this work, we propose a method to observe and analyze redundancies in the weights of a 2D convolutional (Conv2D) network. Based on the proposed analysis, we construct a new layer called Separable Convolutional Eigen-Filters (SCEF) as an alternative parameterization to Conv2D layers. A SCEF layer can be easily implemented using depthwise separable convolution, which are known to be computationally effective. To verify our hypothesis, experiments are conducted on the CIFAR-10 and ImageNet datasets by replacing the Conv2D layers with SCEF and the results have shown an increased accuracy using about 2/3 of the original parameters and reduce the number of FLOPs to 2/3 of the original net. Implementation-wise, our method is highly modular, easy to use, fast to process and does not require any additional dependencies.

Includes members:
  Member "learning to prune filters in convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sasl saliency adaptive sparsity learning for neural network acceleration")
  Member "network compression using correlation analysis of layer responses":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "least squares binary quantization of neural networks")
  Member "extreme network compression via filter group approximation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "lossless compression of deep neural networks")
  Member "nisp pruning networks using neuron importance score propagation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "thinet a filter level pruning method for deep neural network compression":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "gan compression efficient architectures for interactive conditional gans")
  Member "coordinating filters for faster deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "progressive gradient pruning for classification detection and domainadaptation")
  Member "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "lossless compression of deep neural networks")
  Member "an entropy based pruning method for cnn compression":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "progressive gradient pruning for classification detection and domainadaptation")

Question: Please tell me which class the source paper 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)