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 "an optimal control approach to deep learning and applications to discrete weight neural networks"):
  Abstract: Deep learning is formulated as a discrete-time optimal control problem. This allows one to characterize necessary conditions for optimality and develop training algorithms that do not rely on gradients with respect to the trainable parameters. In particular, we introduce the discrete-time method of successive approximations (MSA), which is based on the Pontryagin's maximum principle, for training neural networks. A rigorous error estimate for the discrete MSA is obtained, which sheds light on its dynamics and the means to stabilize the algorithm. The developed methods are applied to train, in a rather principled way, neural networks with weights that are constrained to take values in a discrete set. We obtain competitive performance and interestingly, very sparse weights in the case of ternary networks, which may be useful in model deployment in low-memory devices.

Includes members:
  Member "binaryrelax a relaxation approach for training deep neural networks with quantized weights":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "gradient ell_1 regularization for quantization robustness")
  Member "stable architectures for deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a comprehensive and modularized statistical framework for gradient norm equality in deep neural networks")
  Member "training quantized nets a deeper understanding":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "leanconvnets low cost yet effective convolutional neural networks")
  Member "maximum principle based algorithms for deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "differential dynamic programming neural optimizer")
  Member "reversible architectures for arbitrarily deep residual neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "translating diffusion wavelets and regularisation into residual networks")
  Member "ternary weight networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "sparse weight activation training")
  Member "trained ternary quantization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "gan compression efficient architectures for interactive conditional gans")
  Member "binaryconnect training deep neural networks with binary weights during propagations":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "sparse weight activation training")

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)