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: "nonconvex sparse logistic regression with weakly convex regularization"
  Abstract: In this paper, we propose to fit a sparse logistic regression model by a weakly convex regularized nonconvex optimization problem. The idea is based on the finding that a weakly convex function as an approximation of the l#N# 0#N# pseudo norm is able to better induce sparsity than the commonly used l#N# 1#N# norm. For a class of weakly convex sparsity inducing functions, we prove the nonconvexity of the corresponding problem and study its local optimality conditions and the choice of the regularization parameter. Despite the nonconvexity, a method based on proximal gradient descent is used to solve the general weakly convex sparse logistic regression, and its convergence behavior is studied theoretically. Then, the general framework is applied to a specific weakly convex function, and a local optimality condition and a bound on the logistic loss at a local optimum are provided. The solution method is instantiated in this case as an iterative firm-shrinkage algorithm, and a Nesterov acceleration is used with a convergence guarantee. Its effectiveness is demonstrated in numerical experiments by both randomly generated and real datasets.

Connected through hyperedge HE-1 (induced by source paper "learning aided physical layer authentication as an intelligent process"):
  with members:
    - "reverse k nearest neighbor search over trajectories"
    - "a survey on wireless security technical challenges recent advances and future trends"
    - "rssi based distributed self localization for wireless sensor networks used in precision agriculture"
    - "on the error region for channel estimation based physical layer authentication over rayleigh fading"
    - "security formalizations and their relationships for encryption and key agreement in information theoretic cryptography"

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)