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: "Probabilistic Instance-Based Learning"
  Abstract: Traditional instance-based learning methods base their predictions directly on (training) data that has been stored in the memory. The predictions are based on weighting the contributions of the individual stored instances by a distance function implementing a domain-dependent similarity metrics. This basic approach suffers from three drawbacks: com-putationally expensive prediction when the database grows large, overfitting in the presence of noisy data, and sensitivity to the selection of a proper distance function. We address all these issues by giving a probabilistic interpretation to instance-based learning, where the goal is to approximate predictive distributions of the attributes of interest. In this probabilistic view the instances are not individual data items but probability distributions, and we perform Bayesian inference with a mixture of such prototype distributions. We demonstrate the feasibility of the method empirically for a wide variety of public domain classification data sets.

Connected through hyperedge HE-1 (induced by source paper "Comparing Predictive Inference Methods for Discrete Domains"):
  with members:
    - "Comparing Bayesian Model Class Selection Criteria by Discrete Finite Mixtures"
    - "Constructing Bayesian finite mixture models by the EM algorithm"

Connected through hyperedge HE-2 (induced by source paper "Using Neural Networks for Descriptive Statistical Analysis of Educational Data"):
  with members:
    - "Hierarchical Mixtures of Experts and the EM Algorithm"
    - "A Practical Bayesian Framework for Backprop Networks"

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