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: "The Parameterized Complexity of Sequence Alignment and Consensus"
  Abstract: The Longest common subsequence problem is examined from the point of view of parameterized computational complexity. There are several different ways in which parameters enter the problem, such as the number of sequences to be analyzed, the length of the common subsequence, and the size of the alphabet. Lower bounds on the complexity of this basic problem imply lower bounds on a number of other sequence alignment and consensus problems. At issue in the theory of parameterized complexity is whether a problem which takes input (x; k) can be solved in time f (k) n ff where ff is independent of k (termed fixed-parameter tractability). It can be argued that this is the appropriate asymptotic model of feasible computability for problems for which a small range of parameter values covers important applications | a situation which certainly holds for many problems in biological sequence analysis. Our main results show that: (1) The Longest Common Subsequence (LCS) parameterized by the number of sequences to be analyzed is hard for W [t] for all t. (2) The LCS problem problem, parameterized by the length of the common subsequence, belongs to W [P ] and is hard for W [2]. (3) The LCS problem parameterized both by the number of sequences and the length of the common subsequence, is complete for W [1]. All of the above results are obtained for unrestricted alphabet sizes. For alphabets of a fixed size, problems (2) and (3) are fixed-parameter tractable. We conjecture that (1) remains hard.

Connected through hyperedge HE-1 (induced by source paper "The Hardness of Problems on Thin Colored Graphs"):
  with members:
    - "A Fast Algorithm for the Computation and Enumeration of Perfect Phylogenies"

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: Theory