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: "Computational Learning in Humans and Machines"
  Abstract: In this paper we review research on machine learning and its relation to computational models of human learning. We focus initially on concept induction, examining five main approaches to this problem, then consider the more complex issue of learning sequential behaviors. After this, we compare the rhetoric that sometimes appears in the machine learning and psychological literature with the growing evidence that different theoretical paradigms typically produce similar results. In response, we suggest that concrete computational models, which currently dominate the field, may be less useful than simulations that operate at a more abstract level. We illustrate this point with an abstract simulation that explains a challenging phenomenon in the area of category learning, and we conclude with some general observations about such abstract models.

Connected through hyperedge HE-1 (induced by source paper "An Analysis of Bayesian Classifiers (1988), involves the formulation of average-case models for specific algorithms"):
  with members:
    - "Towards a Better Understanding of Memory-Based Reasoning Systems"
    - "Induction of One-Level Decision Trees"
    - "Average-Case Analysis of a Nearest Neighbor Algorithm"
    - "A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection"
    - "Mining and Model Simplicity: A Case Study in Diagnosis"
    - "Issues in the Integration of Data Mining and Data Visualization Visualizing the Simple Bayesian Classifier"

Connected through hyperedge HE-2 (induced by source paper "Learning Semantic Grammars with Constructive Inductive Logic Programming"):
  with members:
    - "Combining Top-down and Bottom-up Techniques in Inductive Logic Programming"
    - "on Inductive Logic Programming (ILP-95) Inducing Logic Programs without Explicit Negative Examples"
    - "Combining FOIL and EBG to Speed-up Logic Programs"

Connected through hyperedge HE-3 (induced by source paper "A Heuristic Approach to the Discovery of Macro-operators. Machine Learning, 3, 285-317. L e a"):
  with members:
    - "Roles of Macro-Actions in Accelerating Reinforcement Learning"
    - "Utilization Filtering a method for reducing the inherent harmfulness of deductively learned knowledge field of"
    - "The Role of Forgetting in Learning"

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