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: "Cooperative Bayesian and Case-Based Reasoning for Solving Multiagent Planning Tasks"
  Abstract: We describe an integrated problem solving architecture named INBANCA in which Bayesian networks and case-based reasoning (CBR) work cooperatively on multiagent planning tasks. This includes two-team dynamic tasks, and this paper concentrates on simulated soccer as an example. Bayesian networks are used to characterize action selection whereas a case-based approach is used to determine how to implement actions. This paper has two contributions. First, we survey integrations of case-based and Bayesian approaches from the perspective of a popular CBR task decomposition framework, thus explaining what types of integrations have been attempted. This allows us to explain the unique aspects of our proposed integration. Second, we demonstrate how Bayesian nets can be used to provide environmental context, and thus feature selection information, for the case-based reasoner.

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"
    - "Lazy Acquisition of Place Knowledge"
    - "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 "Massively Parallel Case-Based Reasoning with Probabilistic Similarity Metrics"):
  with members:
    - "Integration of Case-Based Reasoning and Neural Networks Approaches for Classification"
    - "A Formal Analysis of Case Base Retrieval"
    - "Bayesian Case-Based Reasoning with Neural Networks"
    - "Learning Bayesian Prototype Trees by Simulated Annealing"
    - "Learning in neural networks with Bayesian prototypes"
    - "MDL Learning of Probabilistic Neural Networks for Discrete Problem Domains"

Connected through hyperedge HE-3 (induced by source paper "Decision-Theoretic Case-Based Reasoning"):
  with members:
    - "Case-Based Probability Factoring in Bayesian Belief Networks"

Connected through hyperedge HE-4 (induced by source paper "LEARNING ROBOT BEHAVIORS USING GENETIC ALGORITHMS"):
  with members:
    - "Learning Sequential Decision Rules Using Simulation Models and Competition"
    - "Using a Genetic Algorithm to Learn Strategies for Collision Avoidance and Local Navigation"
    - "Improving Tactical Plans with Genetic Algorithms"
    - "ROBO-SHEPHERD: LEARNING COMPLEX ROBOTIC BEHAVIORS"
    - "ROBOT LEARNING WITH PARALLEL GENETIC ALGORITHMS ON NETWORKED COMPUTERS"
    - "Simulation-Assisted Learning by Competition: Effects of Noise Differences Between Training Model and Target Environment"
    - "AN ENHANCER FOR REACTIVE PLANS"

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