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 "stagewise knowledge distillation"):
  Abstract: The deployment of modern Deep Learning models requires high computational power. However, many applications are targeted for embedded devices like smartphones and wearables which lack such computational abilities. This necessitates compact networks that reduce computations while preserving the performance. Knowledge Distillation is one of the methods used to achieve this. Traditional Knowledge Distillation methods transfer knowledge from teacher to student in a single stage. We propose progressive stagewise training to improve the transfer of knowledge. We also show that this method works even with a fraction of the data used for training the teacher model, without compromising on the metric. This method can complement other model compression methods and also can be viewed as a generalized model compression technique.

Includes members:
  Member "faster gaze prediction with dense networks and fisher pruning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "towards efficient model compression via learned global ranking")
  Member "deep learning using rectified linear units relu":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "hyper parameter optimization a review of algorithms and applications")
  Member "model compression via distillation and quantization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "gan compression efficient architectures for interactive conditional gans")
  Member "meal multi model ensemble via adversarial learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "reactnet towards precise binary neural network with generalized activation functions")
  Member "attention is all you need":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "apprentice using knowledge distillation techniques to improve low precision network accuracy":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "switchable precision neural networks")
  Member "hardware software codesign of accurate multiplier free deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "alert accurate anytime learning for energy and timeliness")
  Member "n2n learning network to network compression via policy gradient reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "group sparsity the hinge between filter pruning and decomposition for network compression")

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.LG(Machine Learning)