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 "deep model compression distilling knowledge from noisy teachers"):
  Abstract: The remarkable successes of deep learning models#R##N#across various applications have resulted in the design of#R##N#deeper networks that can solve complex problems. How-#R##N#ever, the increasing depth of such models also results in#R##N#a higher storage and runtime complexity, which restricts#R##N#the deployability of such very deep models on mobile and#R##N#portable devices, which have limited storage and battery#R##N#capacity. While many methods have been proposed for deep#R##N#model compression in recent years, almost all of them have#R##N#focused on reducing storage complexity. In this work, we#R##N#extend the teacher-student framework for deep model com-#R##N#pression, since it has the potential to address runtime and#R##N#train time complexity too. We propose a simple method-#R##N#ology to include a noise-based regularizer while training#R##N#the student from the teacher, which provides a healthy im-#R##N#provement in the performance of the student network. Our#R##N#experiments on the CIFAR-10, SVHN and MNIST datasets#R##N#show promising improvement, with the best performance on#R##N#the CIFAR-10 dataset. We also conduct a comprehensive#R##N#empirical evaluation of the proposed method under related#R##N#settings on the CIFAR-10 dataset to show the promise of the#R##N#proposed approach.

Includes members:
  Member "lazy evaluation of convolutional filters":
    also connected through other hyperedges:
      (none)
  Member "tensorizing neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sparse weight activation training")
  Member "data free parameter pruning for deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "privacy preserving learning via deep net pruning")
  Member "compressing neural networks with the hashing trick":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a network pruning network approach to deep model compression")
  Member "adam a method for stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "fitnets hints for thin deep nets":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "switchable precision neural networks")
  Member "very deep convolutional networks for large scale image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "compressing deep convolutional networks using vector quantization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "discrimination aware network pruning for deep model 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)