human: Given a node-centered hypergraph: <hypergraph>, where nodes represent authors and hyperedges represent groups of authors who co-authored the same paper.

Details of the hypergraph:
Center node: "The Convex Geometry of Linear Inverse Problems"
  Abstract: In applications throughout science and engineering one is often faced with the challenge of solving an ill-posed inverse problem, where the number of available measurements is smaller than the dimension of the model to be estimated. However in many practical situations of interest, models are constrained structurally so that they only have a few degrees of freedom relative to their ambient dimension. This paper provides a general framework to convert notions of simplicity into convex penalty functions, resulting in convex optimization solutions to linear, underdetermined inverse problems. The class of simple models considered includes those formed as the sum of a few atoms from some (possibly infinite) elementary atomic set; examples include well-studied cases from many technical fields such as sparse vectors (signal processing, statistics) and low-rank matrices (control, statistics), as well as several others including sums of a few permutation matrices (ranked elections, multiobject tracking), low-rank tensors (computer vision, neuroscience), orthogonal matrices (machine learning), and atomic measures (system identification). The convex programming formulation is based on minimizing the norm induced by the convex hull of the atomic set; this norm is referred to as the atomic norm. The facial structure of the atomic norm ball carries a number of favorable properties that are useful for recovering simple models, and an analysis of the underlying convex geometry provides sharp estimates of the number of generic measurements required for exact and robust recovery of models from partial information. These estimates are based on computing the Gaussian widths of tangent cones to the atomic norm ball. When the atomic set has algebraic structure the resulting optimization problems can be solved or approximated via semidefinite programming. The quality of these approximations affects the number of measurements required for recovery, and this tradeoff is characterized via some examples. Thus this work extends the catalog of simple models (beyond sparse vectors and low-rank matrices) that can be recovered from limited linear information via tractable convex programming.

Connected through hyperedge HE-1 (induced by source paper "High Precision TOA-based Direct Localization of Multiple Sources in Multipath"):
  with members:
    (none)

Connected through hyperedge HE-2 (induced by source paper "Improved approximation algorithms for maximum cut and satisfiability problems using semidefinite programming"):
  with members:
    - "Mechanism Design via Differential Privacy"
    - "On metaheuristic algorithms for combinatorial optimization problems"
    - "Approximate graph coloring by semidefinite programming"
    - "Free bits, PCPs and non-approximability-towards tight results"
    - "An invariance principle for polytopes"
    - "A global continuation algorithm for solving binary quadratic programming problems"
    - "How good is the Goemans-Williamson MAX CUT algorithm?"
    - "Some geometric results in semidefinite programming"

Question: Please tell me which research area the center author belongs to. The 4 classes are: Computer science, Mathematics, Engineering, Economics, Psychology, Business. Directly output the class name.

gpt: Mathematics