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 "model compression as constrained optimization with application to neural nets part i general framework"):
  Abstract: Compressing neural nets is an active research problem, given the large size of state-of-the-art nets for tasks such as object recognition, and the computational limits imposed by mobile devices. We give a general formulation of model compression as constrained optimization. This includes many types of compression: quantization, low-rank decomposition, pruning, lossless compression and others. Then, we give a general algorithm to optimize this nonconvex problem based on the augmented Lagrangian and alternating optimization. This results in a "learning-compression" algorithm, which alternates a learning step of the uncompressed model, independent of the compression type, with a compression step of the model parameters, independent of the learning task. This simple, efficient algorithm is guaranteed to find the best compressed model for the task in a local sense under standard assumptions. #R##N#We present separately in several companion papers the development of this general framework into specific algorithms for model compression based on quantization, pruning and other variations, including experimental results on compressing neural nets and other models.

Includes members:
  Member "model compression as constrained optimization with application to neural nets part ii quantization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "towards hardware implementation of neural network based communication algorithms")
  Member "dorefa net training low bitwidth convolutional neural networks with low bitwidth gradients":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "ternary weight networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sparse weight activation training")
  Member "quantized neural networks training neural networks with low precision weights and activations":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "switchable precision neural networks")
  Member "binaryconnect training deep neural networks with binary weights during propagations":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "sparse weight activation training")
  Member "tensorizing neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "sparse weight activation training")
  Member "learning both weights and connections for efficient neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "compressing neural networks with the hashing trick":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a network pruning network approach to 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)