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 "thanks for nothing predicting zero valued activations with lightweight convolutional neural networks"):
  Abstract: Convolutional neural networks (CNNs) introduce state-of-the-art results for various tasks with the price of high computational demands. Inspired by the observation that spatial correlation exists in CNN output feature maps (ofms), we propose a method to dynamically predict whether ofm activations are zero-valued or not according to their neighboring activation values, thereby avoiding zero-valued activations and reducing the number of convolution operations. We implement the zero activation predictor (ZAP) with a lightweight CNN, which imposes negligible overheads and is easy to deploy and train. Furthermore, the same ZAP can be tuned to many different operating points along the accuracy-savings trade-off curve. For example, using VGG-16 and the ILSVRC-2012 dataset, different operating points achieve a reduction of 23.5% and 32.3% multiply-accumulate (MAC) operations with top-1/top-5 accuracy degradation of 0.3%/0.1% and 1%/0.5% without fine-tuning, respectively. Considering one-epoch fine-tuning, 41.7% MAC operations may be reduced with 1.1%/0.52% accuracy degradation.

Includes members:
  Member "divide and conquer leveraging intermediate feature representations for quantized training of neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "gradient based deep quantization of neural networks through sinusoidal adaptive regularization")
  Member "spatial correlation and value prediction in convolutional neural networks":
    also connected through other hyperedges:
      (none)
  Member "efficient and robust parallel dnn training through model parallelism on multi gpu platform":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "megatron lm training multi billion parameter language models using model parallelism")
  Member "mnasnet platform aware neural architecture search for mobile":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "automl zero evolving machine learning algorithms from scratch")
  Member "dynamic channel pruning feature boosting and suppression":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "effective training of convolutional neural networks with low bitwidth weights and activations")
  Member "an analysis of deep neural network models for practical applications":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "crl class representative learning for image classification")
  Member "sparsenn an energy efficient neural network accelerator exploiting input and output sparsity":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "sparsity in deep neural networks an empirical investigation with tensorquant")
  Member "efficient processing of deep neural networks a tutorial and survey":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "searching for winograd aware quantized networks")

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.CV(Computer Vision and Pattern Recognition)