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 "solving equations of random convex functions via anchored regression"):
  Abstract: We consider the question of estimating a solution to a system of equations that involve convex nonlinearities, a problem that is common in machine learning and signal processing. Because of these nonlinearities, conventional estimators based on empirical risk minimization generally involve solving a non-convex optimization program. We propose anchored regression, a new approach based on convex programming that amounts to maximizing a linear functional (perhaps augmented by a regularizer) over a convex set. The proposed convex program is formulated in the natural space of the problem, and avoids the introduction of auxiliary variables, making it computationally favorable. Working in the native space also provides great flexibility as structural priors (e.g., sparsity) can be seamlessly incorporated. #R##N#For our analysis, we model the equations as being drawn from a fixed set according to a probability law. Our main results provide guarantees on the accuracy of the estimator in terms of the number of equations we are solving, the amount of noise present, a measure of statistical complexity of the random equations, and the geometry of the regularizer at the true solution. We also provide recipes for constructing the anchor vector (that determines the linear functional to maximize) directly from the observed data.

Includes members:
  Member "learning relus via gradient descent":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "mean field analysis of two layer neural networks non asymptotic rates and generalization bounds")
  Member "corruption robust phase retrieval via linear programming":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "branchhull convex bilinear inversion from the entrywise product of signals with known signs")
  Member "compressed sensing from phaseless gaussian measurements via linear programming in the natural parameter space":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "structured signal recovery from quadratic measurements breaking sample complexity barriers via nonconvex optimization")
  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-4 (induced by source paper "a deterministic convergence framework for exact non convex phase retrieval")
  Member "phase retrieval meets statistical learning theory a flexible convex relaxation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "phase retrieval of low rank matrices by anchored regression")
  Member "self calibration and biconvex compressive sensing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "support recovery for sparse signals with unknown non stationary modulation")
  Member "convex recovery of a structured signal from independent random linear measurements":
    also connected through other hyperedges:
      - Hyperedge HE-7 (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-8 (induced by source paper "compressed sensing with 1d total variation breaking sample complexity barriers via non uniform recovery")

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)