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 "convex relaxations of convolutional neural nets"):
  Abstract: We propose convex relaxations for convolutional neural nets with one hidden layer where the output weights are fixed. For convex activation functions such as rectified linear units, the relaxations are convex second order cone programs which can be solved very efficiently. We prove that the relaxation recovers the global minimum under a planted model assumption, given sufficiently many training samples from a Gaussian distribution. We also identify a phase transition phenomenon in recovering the global minimum for the relaxation.

Includes members:
  Member "learning one convolutional layer with overlapping patches":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a deep conditioning treatment of neural networks")
  Member "lifted neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "effects of depth width and initialization a convergence analysis of layer wise training for deep linear neural networks")
  Member "gradient descent learns one hidden layer cnn don t be afraid of spurious local minima":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "the role of invariance in spectral complexity based generalization bounds")
  Member "learning relus via gradient descent":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "mean field analysis of two layer neural networks non asymptotic rates and generalization bounds")
  Member "globally optimal gradient descent for a convnet with gaussian inputs":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "backward feature correction how deep learning performs deep learning")
  Member "reliably learning the relu in polynomial time":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning a single neuron with gradient methods")
  Member "convexified convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "generalised lipschitz regularisation equals distributional robustness")
  Member "randomized sketches of convex programs with sharp guarantees":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "weighted gradient coding with leverage score sampling")

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)