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 the convergence of perturbed distributed asynchronous stochastic gradient descent to second order stationary points in non convex optimization"):
  Abstract: In this paper, the second order convergence of non-convex optimization in asynchronous stochastic gradient descent (ASGD) algorithm is studied systematically. We investigate the behavior of ASGD near and away from saddle points and show that, different from general stochastic gradient descent(SGD), ASGD may return back after escaping the saddle points, yet after staying near a saddle point for a long enough time $(O(T))$, ASGD will finally go away from strictly saddle points. An inequality is given to describe the process of ASGD to escape from saddle points. We show the exponential instability of the perturbed gradient dynamics near the strictly saddle points and use a novel Razumikhin-Lyapunov method to give a more detailed estimation about how the time delay parameter T influence the speed to escape. In particular, we consider the optimization of smooth nonconvex functions, and propose a perturbed asynchronous stochastic gradient descent algorithm with guarantee of convergence to second order stationary points with high probability in $O(1/\epsilon^4)$ iterations. To the best of our knowledge, this is the first work on the second order convergence of asynchronous algorithm.

Includes members:
  Member "escaping saddle points with adaptive gradient methods":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "disentangling adaptive gradient methods from learning rates")
  Member "taming convergence for asynchronous stochastic gradient descent with unbounded delay in non convex learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "compressed distributed gradient descent communication efficient consensus over networks")
  Member "on the power of over parametrization in neural networks with quadratic activation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "knowledge distillation via adaptive instance normalization")
  Member "a hitting time analysis of stochastic gradient langevin dynamics":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "efficient active learning of sparse halfspaces with arbitrary bounded noise")
  Member "global optimality in low rank matrix optimization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "finding the sparsest vectors in a subspace theory algorithms and applications")
  Member "matrix completion has no spurious local minimum":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "finding the sparsest vectors in a subspace theory algorithms and applications")
  Member "escaping from saddle points online stochastic gradient for tensor decomposition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "backward feature correction how deep learning performs deep learning")
  Member "deep learning with elastic averaging sgd":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "scaffold stochastic controlled averaging for federated learning")

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)