human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent groups of papers sharing a common citing paper.

Details of the hypergraph:
Center node: "Nonsmooth Dynamic Simulation With Linear Programming Based Methods"
  Abstract: Process simulation has emerged as a valuable tool for process design, analysis and operation. In this work, we extend the capabilities of iterated linear programming (LP) for dealing with problems encountered in dynamic nonsmooth process simulation. A previously developed LP method is refined with the addition of a new descent strategy which combines line search with a trust region approach. This adds more stability and efficiency to the method. The LP method has the advantage of naturally dealing with profile bounds as well. This is demonstrated to avoid the computational difficulties which arise from the iterates going into physically unrealistic regions. A new method for the treatment of discontinuities occurring in dynamic simulation problems is also presented in this paper. The method ensures that any event which has occurred within the time interval in consideration is detected and if more than one event occurs, the detected one is indeed the earliest one. A specific class of implicitly discontinuous process simulation problems, phase equilibrium calculations is also looked at. A new formulation is introduced to solve multiphase problems. fl To whom all correspondence should be addressed. email:biegler@cmu.edu

Connected through hyperedge HE-1 (induced by source paper "A SUCCESSIVE LINEAR PROGRAMMING APPROACH FOR INITIALIZATION AND REINITIALIZATION AFTER DISCONTINUITIES OF DIFFERENTIAL ALGEBRAIC EQUATIONS"):
  with members:
    (none)

Connected through hyperedge HE-2 (induced by source paper "Data Reconciliation and Gross Error Detection for Dynamic Systems"):
  with members:
    - "Inference in Dynamic Error-in-Variable-Measurement Problems"

Question: Please tell me which class the center node 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