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 "trainable thresholds for neural network quantization"):
  Abstract: Embedded computer vision applications for robotics, security cameras, and mobile phone apps require the usage of mobile neural network architectures like MobileNet-v2 or MNAS-Net in order to reduce RAM consumption and accelerate processing. An additional option for further resource consumption reduction is 8-bit neural network quantization. Unfortunately, the known methods for neural network quantization lead to significant accuracy reduction (more than 1.2%) for mobile architectures and require long training with quantization procedure.

Includes members:
  Member "binary ensemble neural network more bits per network or more networks per bit":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning architectures for binary networks")
  Member "mobilenetv2 inverted residuals and linear bottlenecks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "a quantization friendly separable convolution for mobilenets":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "switchable precision neural networks")
  Member "training wide residual networks for deployment using a single bit for each weight":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "sparse weight activation training")
  Member "nice noise injection and clamping estimation for neural network quantization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "replacing mobile camera isp with a single deep learning model")
  Member "quantization for rapid deployment of deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "post training piecewise linear quantization for deep neural networks")
  Member "mnasnet platform aware neural architecture search for mobile":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "automl zero evolving machine learning algorithms from scratch")
  Member "quantization and training of neural networks for efficient integer arithmetic only inference":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "searching for winograd aware quantized networks")

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)