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: "Massively Parallel Case-Based Reasoning with Probabilistic Similarity Metrics"
  Abstract: We propose a probabilistic case-space metric for the case matching and case adaptation tasks. Central to our approach is a probability propagation algorithm adopted from Bayesian reasoning systems, which allows our case-based reasoning system to perform theoretically sound probabilistic reasoning. The same probability propagation mechanism actually offers a uniform solution to both the case matching and case adaptation problems. We also show how the algorithm can be implemented as a connectionist network, where efficient massively parallel case retrieval is an inherent property of the system. We argue that using this kind of an approach, the difficult problem of case indexing can be completely avoided. Pp. 144-154 in Topics in Case-Based Reasoning, edited by Stefan Wess, Klaus-Dieter Althoff and Michael M. Richter. Volume 837, Lecture

Connected through hyperedge HE-1 (induced by source paper "A Formal Analysis of Case Base Retrieval"):
  with members:
    - "(1994); Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches. Case-Based Reasoning: Foundational Issues, Methodological"
    - "PAC Analyses of a `Similarity Learning' IBL Algorithm"
    - "The case for cases: a call for purity in case-based reasoning inherently more difficult than"
    - "Formalising the knowledge content of case memory systems"

Connected through hyperedge HE-2 (induced by source paper "Bayesian Case-Based Reasoning with Neural Networks"):
  with members:
    - "Learning Bayesian Prototype Trees by Simulated Annealing"
    - "NEULA: A hybrid neural-symbolic expert system shell"
    - "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 "Learning in neural networks with Bayesian prototypes"):
  with members:
    - "Induction of Selective Bayesian Classifiers"
    - "Bayesian Case-Based Reasoning with Neural Networks"
    - "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-4 (induced by source paper "Learning Bayesian Prototype Trees by Simulated Annealing"):
  with members:
    - "Induction of Selective Bayesian Classifiers"
    - "Using Bayesian networks for incorporating probabilistic a priori knowledge into Boltzmann machines"
    - "Bayesian Case-Based Reasoning with Neural Networks"
    - "Learning in neural networks with Bayesian prototypes"

Connected through hyperedge HE-5 (induced by source paper "Cooperative Bayesian and Case-Based Reasoning for Solving Multiagent Planning Tasks"):
  with members:
    - "(1994); Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches. Case-Based Reasoning: Foundational Issues, Methodological"
    - "LEARNING ROBOT BEHAVIORS USING GENETIC ALGORITHMS"
    - "Decision-Theoretic Case-Based Reasoning"

Connected through hyperedge HE-6 (induced by source paper "MDL Learning of Probabilistic Neural Networks for Discrete Problem Domains"):
  with members:
    - "A THEORY OF INFERRED CAUSATION perceive causal relationships in uncon trolled observations. 2. the task"
    - "Induction of Selective Bayesian Classifiers"
    - "Bayesian Case-Based Reasoning with Neural Networks"
    - "Parzen. On estimation of a probability density function and mode. Annual Mathematical Statistics, 33:1065-1076, 1962."

Connected through hyperedge HE-7 (induced by source paper "Integration of Case-Based Reasoning and Neural Networks Approaches for Classification"):
  with members:
    - "(1994); Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches. Case-Based Reasoning: Foundational Issues, Methodological"
    - "An Improved Algorithm for Incremental Induction of Decision Trees"
    - "Inductive Learning and Case-Based Reasoning"
    - "GAL: Networks that grow when they learn and shrink when they forget"

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