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: "Chunking in soar: The anatomy of a general learn ing mechanism. Machine Learning, 1(1). Learning"
  Abstract: gers University. Also appears as tech. report ML- TR-7. Minton, S. (1988). Quantitative results concerning the utility of explanation-based learning. In Proceedings of National Conference on Artificial Intelli gence, pages 564-569. St. Paul, MN.

Connected through hyperedge HE-1 (induced by source paper "Learning Approximate Control Rules Of High Utility"):
  with members:
    - "Utilization Filtering a method for reducing the inherent harmfulness of deductively learned knowledge field of"
    - "Combining FOIL and EBG to Speed-up Logic Programs"
    - "Learning Search-Control Heuristics for Logic Programs: Applications to Speedup Learning and Language Acquisition"

Connected through hyperedge HE-2 (induced by source paper "The Problem with Noise and Small Disjuncts"):
  with members:
    - "Concept Learning and the Problem of Small"
    - "Learning with Rare Cases and Small Disjuncts"

Connected through hyperedge HE-3 (induced by source paper "Learning with Rare Cases and Small Disjuncts"):
  with members:
    - "Concept Learning and the Problem of Small"
    - "The Problem with Noise and Small Disjuncts"

Connected through hyperedge HE-4 (induced by source paper "A systematic description of greedy optimisation algorithms for cost sensitive generalisation"):
  with members:
    - "Cost-Sensitive Classification: Empirical Evaluation of a Hybrid Genetic Decision Tree Induction Algorithm"

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