human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "variance reduction with sparse gradients"):
  with members:
    - "on the ineffectiveness of variance reduced optimization for deep learning"
    - "stochastic nested variance reduction for nonconvex optimization"
    - "sparsified sgd with memory"
    - "don t decay the learning rate increase the batch size"
    - "sparse communication for distributed gradient descent"
    - "deep residual learning for image recognition"
    - "improved svrg for non strongly convex or sum of non convex objectives"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "stochastic nested variance reduction for nonconvex optimization"):
  with members:
    - "convergence analysis of proximal gradient with momentum for nonconvex optimization"
    - "sdca without duality"

Connected through hyperedge HE-3 (induced by source paper "neon2 finding local minima via first order oracles"):
  with members:
    - "follow the compressed leader faster online learning of eigenvectors and faster mmwu"
    - "a generic approach for escaping saddle points"
    - "lazysvd even faster svd decomposition yet without agonizing pain"
    - "sdca without duality regularization and individual convexity"
    - "escaping from saddle points online stochastic gradient for tensor decomposition"
    - "qualitatively characterizing neural network optimization problems"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"

Question: Please tell me which class the center node 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)