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 "metapruning meta learning for automatic neural network channel pruning"):
  Abstract: In this paper, we propose a novel meta learning approach for automatic channel pruning of very deep neural networks. We first train a PruningNet, a kind of meta network, which is able to generate weight parameters for any pruned structure given the target network. We use a simple stochastic structure sampling method for training the PruningNet. Then, we apply an evolutionary procedure to search for good-performing pruned networks. The search is highly efficient because the weights are directly generated by the trained PruningNet and we do not need any finetuning. With a single PruningNet trained for the target network, we can search for various Pruned Networks under different constraints with little human participation. We have demonstrated competitive performances on MobileNet V1/V2 networks, up to 9.0/9.9 higher ImageNet accuracy than V1/V2. Compared to the previous state-of-the-art AutoML-based pruning methods, like AMC and NetAdapt, we achieve higher or comparable accuracy under various conditions.

Includes members:
  Member "meta sr a magnification arbitrary network for super resolution":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "carafe content aware reassembly of features")
  Member "netadapt platform aware neural network adaptation for mobile applications":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "mobilenetv2 inverted residuals and linear bottlenecks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "metaanchor learning to detect objects with customized anchors":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "pointins point based instance segmentation")
  Member "slimmable neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "switchable precision neural networks")
  Member "fbnet hardware aware efficient convnet design via differentiable neural architecture search":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "fast neural network adaptation via parameter remapping and architecture search")
  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 "dpp net device aware progressive search for pareto optimal neural architectures":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "towards efficient model compression via learned global ranking")

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)