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: "Probably Approximately Optimal Derivation Strategies"
  Abstract: An inference graph can have many "derivation strategies", each a particular ordering of the steps involved in reducing a given query to a sequence of database retrievals. An "optimal strategy" for a given distribution of queries is a complete strategy whose "expected cost" is minimal, where the expected cost depends on the conditional probabilities that each requested retrieval succeeds, given that a member of this class of queries is posed. This paper describes the PAO algorithm that first uses a set of training examples to approximate these probability values, and then uses these estimates to produce a "probably approximately optimal" strategy | i.e., given any *; ffi &gt; 0, PAO produces a strategy whose cost is within * of the cost of the optimal strategy, with probability greater than 1 ffi. This paper also shows how to obtain these strategies in time polynomial in 1=*, 1=ffi and the size of the inference graph, for many important classes of graphs, including all and-or trees.

Connected through hyperedge HE-1 (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"
    - "Learning an Optimally Accurate Representational System"
    - "Learning Hierarchical Rule Sets"

Connected through hyperedge HE-2 (induced by source paper "Learning Default Concepts"):
  with members:
    - "A Statistical Approach to Solving the EBL Utility Problem"
    - "Learning Active Classifiers"
    - "ON THE SAMPLE COMPLEXITY OF FINDING GOOD SEARCH STRATEGIES 2n trials of each undetermined experiment"
    - "Knowing What Doesn't Matter: Exploiting Omitted Superfluous Data"

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"
    - "Learning an Optimally Accurate Representational System"
    - "ON THE SAMPLE COMPLEXITY OF FINDING GOOD SEARCH STRATEGIES 2n trials of each undetermined experiment"

Connected through hyperedge HE-4 (induced by source paper "Learning an Optimally Accurate Representational System"):
  with members:
    - "A Statistical Approach to Solving the EBL Utility Problem"
    - "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