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: "Lazy Acquisition of Place Knowledge"
  Abstract: In this paper we define the task of place learning and describe one approach to this problem. Our framework represents distinct places as evidence grids, a probabilistic description of occupancy. Place recognition relies on nearest neighbor classification, augmented by a registration process to correct for translational differences between the two grids. The learning mechanism is lazy in that it involves the simple storage of inferred evidence grids. Experimental studies with physical and simulated robots suggest that this approach improves place recognition with experience, that it can handle significant sensor noise, that it benefits from improved quality in stored cases, and that it scales well to environments with many distinct places. Additional studies suggest that using historical information about the robot's path through the environment can actually reduce recognition accuracy. Previous researchers have studied evidence grids and place learning, but they have not combined these two powerful concepts, nor have they used systematic experimentation to evaluate their methods' abilities.

Connected through hyperedge HE-1 (induced by source paper "(1994); Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches. Case-Based Reasoning: Foundational Issues, Methodological"):
  with members:
    - "Using a Case Base of Surfaces to Speed-Up Reinforcement Learning"
    - "Computer-Supported Argumentation for Cooperative Design on the World-Wide Web"
    - "Cooperative Bayesian and Case-Based Reasoning for Solving Multiagent Planning Tasks"
    - "SaxEx a case-based reasoning system for generating expressive musical performances"
    - "Integration of Case-Based Reasoning and Neural Networks Approaches for Classification"
    - "Machine Learning: An Annotated Bibliography for the 1995 AI Statistics Tutorial on Machine Learning (Version 1)"
    - "NACODAE: Navy Conversational Decision Aids Environment"
    - "Distance Induction in First Order Logic used for classification via a k-nearest-neighbor process. Experiments on"

Connected through hyperedge HE-2 (induced by source paper "Case-Based Acquisition of Place Knowledge"):
  with members:
    - "Exploration and Model Building in Mobile Robot Domains"

Connected through hyperedge HE-3 (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"
    - "LEARNING BY ERROR-DRIVEN DECOMPOSITION"
    - "The Influence of Domain Properties on the Performance of Real-Time Search Algorithms"
    - "Case-Based Acquisition of Place Knowledge"

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