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: "attention based guided structured sparsity of deep neural networks"
  Abstract: Network pruning is aimed at imposing sparsity in a neural network architecture by increasing the portion of zero-valued weights for reducing its size regarding energy-efficiency consideration and increasing evaluation speed. In most of the conducted research efforts, the sparsity is enforced for network pruning without any attention to the internal network characteristics such as unbalanced outputs of the neurons or more specifically the distribution of the weights and outputs of the neurons. That may cause severe accuracy drop due to uncontrolled sparsity. In this work, we propose an attention mechanism that simultaneously controls the sparsity intensity and supervised network pruning by keeping important information bottlenecks of the network to be active. On CIFAR-10, the proposed method outperforms the best baseline method by 6% and reduced the accuracy drop by 2.6x at the same level of sparsity.

Connected through hyperedge HE-1 (induced by source paper "towards compact convnets via structure sparsity regularized filter pruning"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "soft filter pruning for accelerating deep convolutional neural networks"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "learning efficient convolutional networks through network slimming"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "thinet a filter level pruning method for deep neural network compression"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "condensenet an efficient densenet using learned group convolutions"

Connected through hyperedge HE-2 (induced by source paper "neural network distiller a python package for dnn compression research"):
  with members:
    - "fakta an automatic end to end fact checking system"
    - "improving neural network quantization without retraining using outlier channel splitting"
    - "sinreq generalized sinusoidal regularization for low bitwidth deep quantized training"
    - "quantizing deep convolutional networks for efficient inference a whitepaper"
    - "amc automl for model compression and acceleration on mobile devices"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "post training 4 bit quantization of convolution networks for rapid deployment"
    - "wrpn wide reduced precision networks"

Connected through hyperedge HE-3 (induced by source paper "sparse architectures for text independent speaker verification using deep neural networks"):
  with members:
    - "speechpy a library for speech processing and recognition"
    - "text independent speaker verification using 3d convolutional neural networks"
    - "polar coding for achieving the capacity of marginal channels in nonbinary input setting"
    - "voxceleb a large scale speaker identification dataset"
    - "3d convolutional neural networks for cross audio visual matching recognition"
    - "on the construction of polar codes for achieving the capacity of marginal channels"
    - "learning structured sparsity in deep neural networks"
    - "learning both weights and connections for efficient neural networks"

Connected through hyperedge HE-4 (induced by source paper "speechpy a library for speech processing and recognition"):
  with members:
    - "text independent speaker verification using 3d convolutional neural networks"
    - "3d convolutional neural networks for cross audio visual matching recognition"

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)