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 Approximate Control Rules Of High Utility"
  Abstract: One of the difficult problems in the area of explanation based learning is the utility problem; learning too many rules of low utility can lead to swamping, or degradation of performance. This paper introduces two new techniques for improving the utility of learned rules. The first technique is to combine EBL with inductive learning techniques to learn a better set of control rules; the second technique is to use these inductive techniques to learn approximate control rules. The two techniques are synthesized in an algorithm called approximating abductive explanation based learning (AxA-EBL). AxA-EBL is shown to improve substantially over standard EBL in several domains.

Connected through hyperedge HE-1 (induced by source paper "Chunking in soar: The anatomy of a general learn ing mechanism. Machine Learning, 1(1). Learning"):
  with members:
    - "A systematic description of greedy optimisation algorithms for cost sensitive generalisation"
    - "Learning with Rare Cases and Small Disjuncts"
    - "The Problem with Noise and Small Disjuncts"

Connected through hyperedge HE-2 (induced by source paper "Learning Search-Control Heuristics for Logic Programs: Applications to Speedup Learning and Language Acquisition"):
  with members:
    (none)

Connected through hyperedge HE-3 (induced by source paper "Combining FOIL and EBG to Speed-up Logic Programs"):
  with members:
    - "Learning Semantic Grammars with Constructive Inductive Logic Programming"
    - "Learning Goal-Decomposition Rules using Exercises"
    - "Theory-guided Empirical Speedup Learning of Goal Decomposition Rules"
    - "An intelligent search method using Inductive Logic Programming"
    - "Learning First-Order Definitions of Functions"

Connected through hyperedge HE-4 (induced by source paper "Utilization Filtering a method for reducing the inherent harmfulness of deductively learned knowledge field of"):
  with members:
    - "Simple Selection of Utile Control Rules in Speedup Learning"
    - "A Heuristic Approach to the Discovery of Macro-operators. Machine Learning, 3, 285-317. L e a"

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: Case_Based