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: "Finding new rules for incomplete theories: Explicit biases for induction with contextual information. In Proceedings"
  Abstract: addressed in KBANN (which translates a theory into a neural-net, refines it using backpropagation, and then retranslates the result back into rules) by adding extra hidden units and connections to the initial network; however, this would require predetermining the num In this paper, we have presented constructive induction techniques recently added to the EITHER theory refinement system. Intermediate concept utilization employs existing rules in the theory to derive higher-level features for use in induction. Intermediate concept creation employs inverse resolution to introduce new intermediate concepts in order to fill gaps in a theory than span multiple levels. These revisions allow EITHER to make use of imperfect domain theories in the ways typical of previous work in both constructive induction and theory refinement. As a result, EITHER is able to handle a wider range of theory imperfections than any other existing theory refinement system.

Connected through hyperedge HE-1 (induced by source paper "Mingers, 1989 J. Mingers. An empirical comparison of pruning methods for decision tree induction. Machine"):
  with members:
    - "Pessimistic decision tree pruning based on tree size"
    - "Multivariate Decision Trees"
    - "Dynamic Automatic Model Selection"
    - "Induction of Oblique Decision Trees"
    - "Data Analyses Using Simulated Breeding and Inductive Learning Methods"
    - "Learning Decision Trees from Decision Rules:"
    - "The Estimation of Probabilities in Attribute Selection Measures for Decision Tree Induction"
    - "Learning Classification Trees"

Connected through hyperedge HE-2 (induced by source paper "Quantifying Prior Determination Knowledge using the PAC Learning Model"):
  with members:
    - "Cryptographic Limitations on Learning Boolean Formulae and Finite Automata"
    - "Learning from Queries and Examples with Tree-structured Bias"
    - "Learning in the Presence of Prior Knowledge: A Case Study Using Model Calibration"
    - "A Formalization of Explanation-Based Macro-operator Learning"

Connected through hyperedge HE-3 (induced by source paper "Theory Refinement Combining Analytical and Empirical Methods"):
  with members:
    - "An Optimized Theory Revision Module"
    - "The Challenge of Revising an Impure Theory"
    - "Learning Analytically and Inductively"
    - "Finding Accurate Frontiers: A Knowledge-Intensive Approach to Relational Learning"
    - "From Theory Refinement to KB Maintenance: a Position Statement"
    - "Tractability of Theory Patching"
    - "Revising Bayesian Network Parameters Using Backpropagation"
    - "Combining Connectionist and Symbolic Learning to Refine Certainty-Factor Rule Bases"

Connected through hyperedge HE-4 (induced by source paper "Correcting Imperfect Domain Theories: A Knowledge-Level Analysis"):
  with members:
    - "Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming"

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: Rule_Learning