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 "elimination of all bad local minima in deep learning"):
  Abstract: In this paper, we theoretically prove that we can eliminate all suboptimal local minima by adding one neuron per output unit to any deep neural network, for multi-class classification, binary classification, and regression with an arbitrary loss function. At every local minimum of any deep neural network with added neurons, the set of parameters of the original neural network (without added neurons) is guaranteed to be a global minimum of the original neural network. The effects of the added neurons are proven to automatically vanish at every local minimum. Unlike many related results in the literature, our theoretical results are directly applicable to common deep learning tasks because the results only rely on the assumptions that automatically hold in the common tasks. Moreover, we discuss several limitations in eliminating the suboptimal local minima in this manner by providing additional theoretical results and several examples.

Includes members:
  Member "effect of depth and width on local minima in deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "duality of width and depth of neural networks")
  Member "deep learning for classical japanese literature":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "improve sgd training via aligning min batches")
  Member "gradient descent with identity initialization efficiently learns positive definite linear transformations by deep residual networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a deep conditioning treatment of neural networks")
  Member "on the loss landscape of a class of deep neural networks with no bad local valleys":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "understanding global loss landscape of one hidden layer relu neural networks")
  Member "deep neural networks with multi branch architectures are less non convex":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "theoretically principled trade off between robustness and accuracy")
  Member "a convergence theory for deep learning via over parameterization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "backward feature correction how deep learning performs deep learning")
  Member "on the power of over parametrization in neural networks with quadratic activation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "knowledge distillation via adaptive instance normalization")
  Member "are resnets provably better than linear predictors":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "understanding global loss landscape of one hidden layer relu neural 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)