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 "Assessment of candidate pfsa models induced from symbol datasets"):
  Abstract: The induction of the optimal finite state machine explanation from symbol strings is known to be at least NP-complete. However, satisfactory approximately optimal explanations may be found by the use of Evolutionary Programming. It has been shown that an information theoretic measure of finite state machine explanations can be used as the fitness function required for the evaluation of candidate explanations during the search for a near-optimal explanation. It is not obvious from the measure which class of explanation will be favoured over others during the search. By empirical studies it is possible to gain some insight into the dimensions the measure is optimising. In general, for probabilistic finite state machines, explanations assessed by a minimum message length estimator with the minimum number of transitions will be favoured over other explanations. The information measure will also favour explanations with uneven distributions of frequencies on transitions from a node suggesting that repeated sequences in symbol strings will be preferred as an explanation. Approximate bounds for acceptance of explanations and the length of string required for induction to be successful are also derived by considerations of the simplest possible and random explanations and their information measure.

Includes members:
  Member "PFSA Modelling of Behavioural Sequences by Evolutionary Programming Rockhampton, Queensland. (1994) "PFSA Modelling of Behavioural":
    also connected through other hyperedges:
      (none)

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: Theory