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: "Learning to Reason with a Restricted View"
  Abstract: The Learning to Reason framework combines the study of Learning and Reasoning into a single task. Within it, learning is done specifically for the purpose of reasoning with the learned knowledge. Computational considerations show that this is a useful paradigm; in some cases learning and reasoning problems that are intractable when studied separately become tractable when performed as a task of Learning to Reason. In this paper we study Learning to Reason problems where the interaction with the world supplies the learner only partial information in the form of partial assignments. Several natural interpretations of partial assignments are considered and learning and reasoning algorithms using these are developed. The results presented exhibit a tradeoff between learnability, the strength of the oracles used in the interface, and the range of reasoning queries the learner is guaranteed to answer correctly.

Connected through hyperedge HE-1 (induced by source paper "Cognitive Computation (Extended Abstract)"):
  with members:
    - "Toward Efficient Agnostic Learning"

Connected through hyperedge HE-2 (induced by source paper "Learning Active Classifiers"):
  with members:
    - "Cost-Sensitive Classification: Empirical Evaluation of a Hybrid Genetic Decision Tree Induction Algorithm"
    - "Exploiting the Omission of Irrelevant Data"
    - "Learning Default Concepts"
    - "Theory and Applications of Agnostic PAC-Learning with Small Decision Trees"

Connected through hyperedge HE-3 (induced by source paper "On the Hardness of Approximate Reasoning"):
  with members:
    (none)

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