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 "dbp discrimination based block level pruning for deep model acceleration"):
  Abstract: Neural network pruning is one of the most popular methods of accelerating the inference of deep convolutional neural networks (CNNs). The dominant pruning methods, filter-level pruning methods, evaluate their performance through the reduction ratio of computations and deem that a higher reduction ratio of computations is equivalent to a higher acceleration ratio in terms of inference time. However, we argue that they are not equivalent if parallel computing is considered. Given that filter-level pruning only prunes filters in layers and computations in a layer usually run in parallel, most computations reduced by filter-level pruning usually run in parallel with the un-reduced ones. Thus, the acceleration ratio of filter-level pruning is limited. To get a higher acceleration ratio, it is better to prune redundant layers because computations of different layers cannot run in parallel. In this paper, we propose our Discrimination based Block-level Pruning method (DBP). Specifically, DBP takes a sequence of consecutive layers (e.g., Conv-BN-ReLu) as a block and removes redundant blocks according to the discrimination of their output features. As a result, DBP achieves a considerable acceleration ratio by reducing the depth of CNNs. Extensive experiments show that DBP has surpassed state-of-the-art filter-level pruning methods in both accuracy and acceleration ratio. Our code will be made available soon.

Includes members:
  Member "towards optimal structured cnn pruning via generative adversarial learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "similarity preserving knowledge distillation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "group sparsity the hinge between filter pruning and decomposition for network compression")
  Member "cop customized deep model compression via regularized correlation based filter level pruning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "channel pruning via automatic structure search")
  Member "greedy layerwise learning can scale to imagenet":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "supervised deep similarity matching")
  Member "mobilenetv2 inverted residuals and linear bottlenecks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "filter pruning via geometric median for deep convolutional neural networks acceleration":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "knowledge distillation with adversarial samples supporting decision boundary":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "search for better students to learn distilled knowledge")
  Member "adversarial network compression":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "residual knowledge distillation")

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)