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: "proximal iteratively reweighted algorithm with multiple splitting for nonconvex sparsity optimization"
  Abstract: This paper proposes the Proximal Iteratively REweighted (PIRE) algorithm for solving a general problem, which involves a large body of nonconvex sparse and structured sparse related problems. Comparing with previous iterative solvers for nonconvex sparse problem, PIRE is much more general and efficient. The computational cost of PIRE in each iteration is usually as low as the state-of-the-art convex solvers. We further propose the PIRE algorithm with Parallel Splitting (PIRE-PS) and PIRE algorithm with Alternative Updating (PIRE-AU) to handle the multi-variable problems. In theory, we prove that our proposed methods converge and any limit solution is a stationary point. Extensive experiments on both synthesis and real data sets demonstrate that our methods achieve comparative learning performance, but are much more efficient, by comparing with previous nonconvex solvers.

Connected through hyperedge HE-1 (induced by source paper "sparse optimization for green edge ai inference"):
  with members:
    - "edge intelligence paving the last mile of artificial intelligence with edge computing"
    - "exploiting computation replication for mobile edge computing a fundamental computation communication tradeoff study"
    - "edge ai on demand accelerating deep neural network inference via edge computing"
    - "mobile edge intelligence and computing for the internet of vehicles"
    - "energy efficient processing and robust wireless cooperative transmission for edge inference"
    - "the roadmap to 6g ai empowered wireless networks"
    - "admm nn an algorithm hardware co design framework of dnns using alternating direction method of multipliers"
    - "data shuffling in wireless distributed computing via low rank optimization"

Connected through hyperedge HE-2 (induced by source paper "nonconvex and nonsmooth sparse optimization via adaptively iterative reweighted methods"):
  with members:
    - "sparse signal processing for grant free massive connectivity a future paradigm for random access protocols in the internet of things"
    - "generalized sparse and low rank optimization for ultra dense networks"
    - "content centric sparse multicast beamforming for cache enabled cloud ran"
    - "iterative concave rank approximation for recovering low rank matrices"
    - "group sparse beamforming for green cloud ran"
    - "optimization with sparsity inducing penalties"

Connected through hyperedge HE-3 (induced by source paper "iteratively linearized reweighted alternating direction method of multipliers for a class of nonconvex problems"):
  with members:
    - "an alternating direction algorithm for matrix completion with nonnegative factors"

Connected through hyperedge HE-4 (induced by source paper "smoothed low rank and sparse matrix recovery by iteratively reweighted least squares minimization"):
  with members:
    - "correlation adaptive subspace segmentation by trace lasso"
    - "generalized nonconvex nonsmooth low rank minimization"
    - "robust and efficient subspace segmentation via least squares regression"
    - "generalized singular value thresholding"
    - "matrix completion with noise"
    - "robust principal component analysis"
    - "exact matrix completion via convex optimization"

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.NA(Numerical Analysis)