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 "katyusha x practical momentum method for stochastic sum of nonconvex optimization"):
  Abstract: The problem of minimizing sum-of-nonconvex functions (i.e., convex functions that are average of non-convex ones) is becoming increasingly important in machine learning, and is the core machinery for PCA, SVD, regularized Newton's method, accelerated non-convex optimization, and more. #R##N#We show how to provably obtain an accelerated stochastic algorithm for minimizing sum-of-nonconvex functions, by $\textit{adding one additional line}$ to the well-known SVRG method. This line corresponds to momentum, and shows how to directly apply momentum to the finite-sum stochastic minimization of sum-of-nonconvex functions. As a side result, our method enjoys linear parallel speed-up using mini-batch.

Includes members:
  Member "how to make the gradients small stochastically":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "asymmetric valleys beyond sharp and flat local minima")
  Member "follow the compressed leader faster online learning of eigenvectors and faster mmwu":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "distributed stochastic algorithms for high rate streaming principal component analysis")
  Member "lazysvd even faster svd decomposition yet without agonizing pain":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "backward feature correction how deep learning performs deep learning")
  Member "sdca without duality regularization and individual convexity":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on the complexity of minimizing convex finite sums without using the indices of the individual functions")
  Member "fast stochastic algorithms for svd and pca convergence properties and convexity":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "distributed stochastic algorithms for high rate streaming principal component analysis")
  Member "improved svrg for non strongly convex or sum of non convex objectives":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "variance reduction with sparse gradients")
  Member "a stochastic pca and svd algorithm with an exponential convergence rate":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning a single neuron with gradient methods")

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)