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 "an incremental path following splitting method for linearly constrained nonconvex nonsmooth programs"):
  Abstract: The linearly constrained nonconvex nonsmooth program has drawn much attention over the last few years due to its ubiquitous power of modeling in the area of machine learning. A variety of important problems, including deep learning, matrix factorization, and phase retrieval, can be reformulated as the problem of optimizing a highly nonconvex and nonsmooth objective function with some linear constraints. However, it is challenging to solve a linearly constrained nonconvex nonsmooth program, which is much complicated than its unconstrained counterpart. In fact, the feasible region is a polyhedron, where a simple projection is intractable in general. In addition, the per-iteration cost is extremely expensive for the high-dimensional case. Therefore, it has been recognized promising to develop a provable and practical algorithm for linearly constrained nonconvex nonsmooth programs. #R##N#In this paper, we develop an incremental path-following splitting algorithm with a theoretical guarantee and a low computational cost. In specific, we show that this algorithm converges to an $\epsilon$-approximate stationary solution within $O(1/\epsilon)$ iterations, and that the per-iteration cost is very small for the randomized variable selection rule. To the best of our knowledge, this is the first incremental method to solve linearly constrained nonconvex nonsmooth programs with a theoretical guarantee. Experiments conducted on the constrained concave penalized linear regression (CCPLR) and nonconvex support vector machine (NCSVM) demonstrate that the proposed algorithm is more effective and stable than other competing heuristic methods.

Includes members:
  Member "on graduated optimization for stochastic non convex problems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "distributed stochastic algorithms for high rate streaming principal component analysis")
  Member "a general iterative shrinkage and thresholding algorithm for non convex regularized optimization problems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "binary matrix completion with nonconvex regularizers")

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)