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 "a network pruning network approach to deep model compression"):
  Abstract: We present a filter pruning approach for deep model compression, using a multitask network. Our approach is based on learning a a pruner network to prune a pre-trained target network. The pruner is essentially a multitask deep neural network with binary outputs that help identify the filters from each layer of the original network that do not have any significant contribution to the model and can therefore be pruned. The pruner network has the same architecture as the original network except that it has a multitask/multi-output last layer containing binary-valued outputs (one per filter), which indicate which filters have to be pruned. The pruner's goal is to minimize the number of filters from the original network by assigning zero weights to the corresponding output feature-maps. In contrast to most of the existing methods, instead of relying on iterative pruning, our approach can prune the network (original network) in one go and, moreover, does not require specifying the degree of pruning for each layer (and can learn it instead). The compressed model produced by our approach is generic and does not need any special hardware/software support. Moreover, augmenting with other methods such as knowledge distillation, quantization, and connection pruning can increase the degree of compression for the proposed approach. We show the efficacy of our proposed approach for classification and object detection tasks.

Includes members:
  Member "cooperative initialization based deep neural network training":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "accuracy booster performance boosting using feature map re calibration")
  Member "accuracy booster performance boosting using feature map re calibration":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "cooperative initialization based deep neural network training")
  Member "cpwc contextual point wise convolution for object recognition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "cooperative initialization based deep neural network training")
  Member "play and prune adaptive filter pruning for deep model compression":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "channel pruning via automatic structure search")
  Member "stability based filter pruning for accelerating deep cnns":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "cooperative initialization based deep neural network training")
  Member "multi layer pruning framework for compressing single shot multibox detector":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "cooperative initialization based deep neural network training")
  Member "amc automl for model compression and acceleration on mobile devices":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "leveraging filter correlations for deep model compression":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "cooperative initialization based deep neural network training")

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)