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: "Bayesian Case-Based Reasoning with Neural Networks"
  Abstract: Given a problem, a case-based reasoning (CBR) system will search its case memory and use the stored cases to find the solution, possibly modifying retrieved cases to adapt to the required input specifications. In this paper we introduce a neural network architecture for efficient case-based reasoning. We show how a rigorous Bayesian probability propagation algorithm can be implemented as a feedforward neural network and adapted for CBR. In our approach the efficient indexing problem of CBR is naturally implemented by the parallel architecture, and heuristic matching is replaced by a probability metric. This allows our CBR to perform theoretically sound Bayesian reasoning. We also show how the probability propagation actually offers a solution to the adaptation problem in a very natural way.

Connected through hyperedge HE-1 (induced by source paper "Learning in neural networks with Bayesian prototypes"):
  with members:
    - "Massively Parallel Case-Based Reasoning with Probabilistic Similarity Metrics"
    - "Induction of Selective Bayesian Classifiers"
    - "Learning Bayesian Prototype Trees by Simulated Annealing"
    - "Parzen. On estimation of a probability density function and mode. Annual Mathematical Statistics, 33:1065-1076, 1962."

Connected through hyperedge HE-2 (induced by source paper "Learning Bayesian Prototype Trees by Simulated Annealing"):
  with members:
    - "Massively Parallel Case-Based Reasoning with Probabilistic Similarity Metrics"
    - "Induction of Selective Bayesian Classifiers"
    - "Using Bayesian networks for incorporating probabilistic a priori knowledge into Boltzmann machines"
    - "Learning in neural networks with Bayesian prototypes"

Connected through hyperedge HE-3 (induced by source paper "Massively Parallel Case-Based Reasoning with Probabilistic Similarity Metrics"):
  with members:
    - "Cooperative Bayesian and Case-Based Reasoning for Solving Multiagent Planning Tasks"
    - "Integration of Case-Based Reasoning and Neural Networks Approaches for Classification"
    - "A Formal Analysis of Case Base Retrieval"
    - "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-4 (induced by source paper "NEULA: A hybrid neural-symbolic expert system shell"):
  with members:
    (none)

Connected through hyperedge HE-5 (induced by source paper "MDL Learning of Probabilistic Neural Networks for Discrete Problem Domains"):
  with members:
    - "Massively Parallel Case-Based Reasoning with Probabilistic Similarity Metrics"
    - "A THEORY OF INFERRED CAUSATION perceive causal relationships in uncon trolled observations. 2. the task"
    - "Induction of Selective Bayesian Classifiers"
    - "Parzen. On estimation of a probability density function and mode. Annual Mathematical Statistics, 33:1065-1076, 1962."

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: Probabilistic_Methods