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 "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.

Includes members:
  Member "an elementary proof of convex phase retrieval in the natural parameter space via the linear program phasemax":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a deterministic convergence framework for exact non convex phase retrieval")
  Member "convex recovery of a structured signal from independent random linear measurements":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "compressed sensing with 1d total variation breaking sample complexity barriers via non uniform recovery")
  Member "the generalized lasso with non linear observations":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "compressed sensing with 1d total variation breaking sample complexity barriers via non uniform recovery")
  Member "learning without concentration":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning with correntropy induced losses for regression with mixture of symmetric stable noise")

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)