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 "escaping saddles with stochastic gradients"):
  Abstract: We analyze the variance of stochastic gradients along negative curvature directions in certain non-convex machine learning models and show that stochastic gradients exhibit a strong component along these directions. Furthermore, we show that - contrary to the case of isotropic noise - this variance is proportional to the magnitude of the corresponding eigenvalues and not decreasing in the dimensionality. Based upon this observation we propose a new assumption under which we show that the injection of explicit, isotropic noise usually applied to make gradient descent escape saddle points can successfully be replaced by a simple SGD step. Additionally - and under the same condition - we derive the first convergence rate for plain SGD to a second-order stationary point in a number of iterations that is independent of the problem dimension.

Includes members:
  Member "neon2 finding local minima via first order oracles":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on nonconvex optimization for machine learning gradients stochasticity and saddle points")
  Member "accelerated gradient descent escapes saddle points faster than gradient descent":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "gradient descent with momentum to accelerate or to super accelerate")
  Member "a hitting time analysis of stochastic gradient langevin dynamics":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "efficient active learning of sparse halfspaces with arbitrary bounded noise")
  Member "a generic approach for escaping saddle points":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on gradient based learning in continuous games")
  Member "stochastic gradient descent performs variational inference converges to limit cycles for deep networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "the implicit and explicit regularization effects of dropout")
  Member "sub sampled cubic regularization for non convex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "stochastic newton and cubic newton methods with simple local linear quadratic rates")
  Member "on the gap between strict saddles and true convexity an omega log d lower bound for eigenvector approximation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "the complexity of finding stationary points with stochastic gradient descent")
  Member "how to escape saddle points efficiently":
    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)