human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups papers that share a common citing paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "A SUCCESSIVE LINEAR PROGRAMMING APPROACH FOR INITIALIZATION AND REINITIALIZATION AFTER DISCONTINUITIES OF DIFFERENTIAL ALGEBRAIC EQUATIONS"):
  Abstract: Determination of consistent initial conditions is an important aspect of the solution of differential algebraic equations (DAEs). Specification of inconsistent initial conditions, even if they are slightly inconsistent, often leads to a failure in the initialization problem. In this paper, we present a Successive Linear Programming (SLP) approach for the solution of the DAE derivative array equations for the initialization problem. The SLP formulation handles roundoff errors and inconsistent user specifications among others and allows for reliable convergence strategies that incorporate variable bounds and trust region concepts. A new consistent set of initial conditions is obtained by minimizing the deviation of the variable values from the specified ones. For problems with discontinuities caused by a step change in the input functions, a new criterion is presented for identifying the subset of variables which are continuous across the discontinuity. The LP formulation is then applied to determine a consistent set of initial conditions for further solution of the problem in the domain after the discontinuity. Numerous example problems are solved to illustrate these concepts.

Includes members:
  Member "Nonsmooth Dynamic Simulation With Linear Programming Based Methods":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Data Reconciliation and Gross Error Detection for Dynamic Systems")

Question: Please tell me which class the citing paper (i.e., the source paper of the center hyperedge) belongs to. The 7 classes are: Neural_Networks, Reinforcement_Learning, Probabilistic_Methods, Theory, Genetic_Algorithms, Case_Based, Rule_Learning. Directly output the class name.

gpt: Neural_Networks