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 an Optimally Accurate Representational System"
  Abstract: The multiple extension problem arises because a default theory can use different subsets of its defaults to propose different, mutually incompatible, answers to some queries. This paper presents an algorithm that uses a set of observations to learn a credulous version of this default theory that is (essentially) "optimally accurate". In more detail, we can associate a given default theory with a set of related credulous theories R = fR i g, where each R i uses its own total ordering of the defaults to determine which single answer to return for each query. Our goal is to select the credulous theory that has the highest "expected accuracy", where each R i 's expected accuracy is the probability that the answer it produces to a query will correspond correctly to the world. Unfortunately, a theory's expected accuracy depends on the distribution of queries, which is usually not known. Moreover, the task of identifying the optimal R opt 2 R, even given that distribution information, is intractable. This paper presents a method, OptAcc, that sidesteps these problems by using a set of samples to estimate the unknown distribution, and by hill-climbing to a local optimum. In particular, given any parameters *; ffi &gt; 0, OptAcc produces an R oa 2 R whose expected accuracy is, with probability at least 1 ffi, within * of a local optimum. Appeared in ECAI Workshop on Theoretical Foundations of Knowledge Representation and Reasoning,

Connected through hyperedge HE-1 (induced by source paper "Probably Approximately Optimal Derivation Strategies"):
  with members:
    - "A Statistical Approach to Solving the EBL Utility Problem"
    - "Learning Default Concepts"
    - "ON THE SAMPLE COMPLEXITY OF FINDING GOOD SEARCH STRATEGIES 2n trials of each undetermined experiment"

Connected through hyperedge HE-2 (induced by source paper "ON THE SAMPLE COMPLEXITY OF FINDING GOOD SEARCH STRATEGIES 2n trials of each undetermined experiment"):
  with members:
    - "A Statistical Approach to Solving the EBL Utility Problem"
    - "Learning Default Concepts"
    - "Probably Approximately Optimal Derivation Strategies"
    - "Learning Hierarchical Rule Sets"

Connected through hyperedge HE-3 (induced by source paper "A Statistical Approach to Solving the EBL Utility Problem"):
  with members:
    - "A Formal Framework for Speedup Learning from Problems and Solutions"
    - "Simple Selection of Utile Control Rules in Speedup Learning"
    - "Hoeffding Races: Accelerating Model Selection Search for Classification and Function Approximation"
    - "Learning Default Concepts"
    - "Probably Approximately Optimal Derivation Strategies"
    - "ON THE SAMPLE COMPLEXITY OF FINDING GOOD SEARCH STRATEGIES 2n trials of each undetermined experiment"

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