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 "on dropout and nuclear norm regularization"):
  Abstract: We give a formal and complete characterization of the explicit regularizer induced by dropout in deep linear networks with squared loss. We show that (a) the explicit regularizer is composed of an $\ell_2$-path regularizer and other terms that are also re-scaling invariant, (b) the convex envelope of the induced regularizer is the squared nuclear norm of the network map, and (c) for a sufficiently large dropout rate, we characterize the global optima of the dropout objective. We validate our theoretical findings with empirical results.

Includes members:
  Member "stochastic gradient mirror descent minimax optimality and implicit regularization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on the distance between two neural networks and the stability of learning")
  Member "implicit self regularization in deep neural networks evidence from random matrix theory and implications for learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "self orthogonality module a network architecture plug in for learning orthogonal filters")
  Member "implicit bias of gradient descent on linear convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "disentangling adaptive gradient methods from learning rates")
  Member "on the implicit bias of dropout":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the implicit and explicit regularization effects of dropout")
  Member "gradient descent aligns the layers of deep linear networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on the global convergence of training deep linear resnets")
  Member "on the relationship between dropout and equiangular tight frames":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "dropout explicit forms and capacity control")
  Member "a convergence analysis of gradient descent for deep linear neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a deep conditioning treatment of neural networks")
  Member "a comprehensive analysis of deep regression":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "cahphf context aware hierarchical qos prediction with hybrid filtering")

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)