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: "Integration of Case-Based Reasoning and Neural Networks Approaches for Classification"
  Abstract: Several different approaches have been used to describe concepts for supervised learning tasks. In this paper we describe two approaches which are: prototype-based incremental neural networks and case-based reasoning approaches. We show then how we can improve a prototype-based neural network model by storing some specific instances in a CBR memory system. This leads us to propose a co-processing hybrid model for classification. 1

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"
    - "Cooperative Bayesian and Case-Based Reasoning for Solving Multiagent Planning Tasks"
    - "SaxEx a case-based reasoning system for generating expressive musical performances"
    - "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 "GAL: Networks that grow when they learn and shrink when they forget"):
  with members:
    - "Cholinergic suppression of transmission may allow combined associative memory function and self-organization in the neocortex."
    - "Book Review  Introduction to the Theory of Neural Computation Reviewed by: 2"
    - "Learning Controllers for Industrial Robots"
    - "Comparison of Kernel Estimators, Perceptrons, and Radial-Basis Functions for OCR and Speech Classification"

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"
    - "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-4 (induced by source paper "Inductive Learning and Case-Based Reasoning"):
  with members:
    - "Lazy Induction Triggered by CBR"
    - "A Case-Based Reasoning Approach"

Connected through hyperedge HE-5 (induced by source paper "An Improved Algorithm for Incremental Induction of Decision Trees"):
  with members:
    - "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,"
    - "Theory Revision in Fault Hierarchies"
    - "Lazy Induction Triggered by CBR"
    - "Induction of Oblique Decision Trees"
    - "Learning Classification Trees"
    - "Relating Relational Learning Algorithms"
    - "Geometric Comparison of Classifications and Rule Sets*"
    - "What Daimler-Benz has learned as an industrial partner from the Machine Learning Project StatLog"

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