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: "Case-Based Acquisition of Place Knowledge"
  Abstract: In this paper we define the task of place learning and describe one approach to this problem. The framework represents distinct places using evidence grids, a probabilistic description of occupancy. Place recognition relies on case-based classification, augmented by a registration process to correct for translations. The learning mechanism is also similar to that in case-based systems, involving the simple storage of inferred evidence grids. Experimental studies with both physical and simulated robots suggest that this approach improves place recognition with experience, that it can handle significant sensor noise, and that it scales well to increasing numbers of places. Previous researchers have studied evidence grids and place learning, but they have not combined these two powerful concepts, nor have they used the experimental methods of machine learning to evaluate their methods' abilities.

Connected through hyperedge HE-1 (induced by source paper "Lazy Acquisition of Place Knowledge"):
  with members:
    - "(1994); Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches. Case-Based Reasoning: Foundational Issues, Methodological"
    - "Exploration and Model Building in Mobile Robot Domains"

Connected through hyperedge HE-2 (induced by source paper "Exploration and Model Building in Mobile Robot Domains"):
  with members:
    - "Learning to Act using Real-Time Dynamic Programming"
    - "Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming"
    - "The Efficient Learning of Multiple Task Sequences"
    - "A Modular Q-Learning Architecture for Manipulator Task Decomposition `Data storage in the cerebellar model ar"
    - "Lazy Acquisition of Place Knowledge"
    - "LEARNING BY ERROR-DRIVEN DECOMPOSITION"
    - "The Influence of Domain Properties on the Performance of Real-Time Search Algorithms"

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