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: "anchored regression solving random convex equations via convex programming"
  Abstract: We consider the problem of estimating a solution to (random) systems of equations that involve convex nonlinearities which has applications in machine learning and signal processing. Conventional estimators based on empirical risk minimization generally lead to non-convex programs that are often computationally intractable. We propose anchored regression, a new approach that utilizes an anchor vector to formulate an estimator based on a simple convex program. We analyze accuracy of this method and specify the required sample complexity. The proposed convex program is formulated in the natural space of the problem rather than a lifted domain, which makes it computationally favorable. This feature of anchored regression also provides great flexibility as structural priors (e.g., sparsity) can be seamlessly incorporated through convex regularization. We also provide recipes for constructing the anchor vector from the data.

Connected through hyperedge HE-1 (induced by source paper "simultaneous phase retrieval and blind deconvolution via convex programming"):
  with members:
    - "blind deconvolutional phase retrieval via convex programming"
    - "benchmark problems for phase retrieval"
    - "phasemax convex phase retrieval via basis pursuit"
    - "phase retrieval meets statistical learning theory a flexible convex relaxation"
    - "convex recovery of a structured signal from independent random linear measurements"
    - "phase retrieval from coded diffraction patterns"
    - "blind deconvolution using convex programming"
    - "phaselift exact and stable signal recovery from magnitude measurements via convex programming"

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)