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: "Rule Generation and Compaction in the wwtp"
  Abstract: In this paper we discuss our approach to learning classification rules from data. We sketch out two modules of our architecture, namely LINNEO + and GAR. LINNEO + , which is a knowledge acquisition tool for ill-structured domains automatically generating classes from examples that incrementally works with an unsupervised strategy. LINNEO + 's output, a representation of the conceptual structure of the domain in terms of classes, is the input to GAR that is used to generate a set of classification rules for the original training set. GAR can generate both conjunctive and disjunctive rules. Herein we present an application of these techniques to data obtained from a real wastewater treatment plant in order to help the construction of a rule base. This rule will be used for a knowledge-based system that aims to supervise the whole process.

Connected through hyperedge HE-1 (induced by source paper "LINNEO A Classification Methodology for Ill-structured Domains"):
  with members:
    (none)

Connected through hyperedge HE-2 (induced by source paper "Rule Induction and Instance-Based Learning: A Unified Approach"):
  with members:
    - "Distance Induction in First Order Logic used for classification via a k-nearest-neighbor process. Experiments on"
    - "Using Partitioning to Speed Up Specific-to-General Rule Induction"
    - "Inductive CBR for Customer Support"

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