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 Complexity of Theory Revision"
  Abstract: A knowledge-based system uses its database (a.k.a. its "theory") to produce answers to the queries it receives. Unfortunately, these answers may be incorrect if the underlying theory is faulty. Standard "theory revision" systems use a given set of "labeled queries" (each a query paired with its correct answer) to transform the given theory, by adding and/or deleting either rules and/or antecedents, into a related theory that is as accurate as possible. After formally defining the theory revision task and bounding its sample complexity, this paper addresses the task's computational complexity. It first proves that, unless P = N P , no polynomial time algorithm can identify the optimal theory, even given the exact distribution of queries, except in the most trivial of situations. It also shows that, except in such trivial situations, no polynomial-time algorithm can produce a theory whose inaccuracy is even close (i.e., within a particular polynomial factor) to optimal. These results justify the standard practice of hill-climbing to a locally-optimal theory, based on a given set of labeled sam ples.

Connected through hyperedge HE-1 (induced by source paper "An Optimized Theory Revision Module"):
  with members:
    - "Theory Revision in Fault Hierarchies"
    - "Irrelevant Features and the Subset Selection Problem"
    - "Theory Refinement Combining Analytical and Empirical Methods"

Connected through hyperedge HE-2 (induced by source paper "The Challenge of Revising an Impure Theory"):
  with members:
    - "Theory Revision in Fault Hierarchies"
    - "Theory Refinement Combining Analytical and Empirical Methods"
    - "The Difficulties of Learning Logic Programs with Cut"

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