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: "On the Informativeness of the DNA Promoter Sequences Domain Theory"
  Abstract: The DNA promoter sequences domain theory and database have become popular for testing systems that integrate empirical and analytical learning. This note reports a simple change and reinterpretation of the domain theory in terms of M-of-N concepts, involving no learning, that results in an accuracy of 93.4% on the 106 items of the database. Moreover, an exhaustive search of the space of M-of-N domain theory interpretations indicates that the expected accuracy of a randomly chosen interpretation is 76.5%, and that a maximum accuracy of 97.2% is achieved in 12 cases. This demonstrates the informativeness of the domain theory, without the complications of understanding the interactions between various learning algorithms and the theory. In addition, our results help characterize the difficulty of learning using the DNA promoters theory.

Connected through hyperedge HE-1 (induced by source paper "Comparing Methods for Refining Certainty-Factor Rule-Bases"):
  with members:
    - "Combining Connectionist and Symbolic Learning to Refine Certainty-Factor Rule Bases"
    - "Bias-Driven Revision of Logical Domain Theories"
    - "FONN: Combining First Order Logic with Connectionist Learning"

Connected through hyperedge HE-2 (induced by source paper "A Case-Based Reasoning Approach"):
  with members:
    - "Inductive Learning and Case-Based Reasoning"
    - "A Similarity-Based Retrieval Tool for Software Repositories"
    - "Applying Case-Based Reasoning to Control in Robotics"

Connected through hyperedge HE-3 (induced by source paper "Bias-Driven Revision of Logical Domain Theories"):
  with members:
    - "Theory Refinement Combining Analytical and Empirical Methods"
    - "Tractability of Theory Patching"
    - "Combining Connectionist and Symbolic Learning to Refine Certainty-Factor Rule Bases"
    - "Comparing Methods for Refining Certainty-Factor Rule-Bases"

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