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: "How to Get a Free Lunch: A Simple Cost Model for Machine Learning Applications"
  Abstract: This paper proposes a simple cost model for machine learning applications based on the notion of net present value. The model extends and unifies the models used in (Pazzani et al., 1994) and (Masand & Piatetsky-Shapiro, 1996). It attempts to answer the question "Should a given machine learning system now in the prototype stage be fielded?" The model's inputs are the system's confusion matrix, the cash flow matrix for the application, the cost per decision, the one-time cost of deploying the system, and the rate of return on investment. Like Provost and Fawcett's (1997) ROC convex hull method, the present model can be used for decision-making even when its input variables are not known exactly. Despite its simplicity, it has a number of non-trivial consequences. For example, under it the "no free lunch" theorems of learning theory no longer apply.

Connected through hyperedge HE-1 (induced by source paper "In Machine Learning: A Multistrategy Approach, Vol. IV  Macro and Micro Perspectives of Multistrategy Learning"):
  with members:
    - "21 Using n 2 classifier in constructive induction"
    - "INFERENTIAL THEORY OF LEARNING: Developing Foundations for Multistrategy Learning"
    - "Learning to Integrate Multiple Knowledge Sources for Case-Based Reasoning"

Connected through hyperedge HE-2 (induced by source paper "Cost-Sensitive Classification: Empirical Evaluation of a Hybrid Genetic Decision Tree Induction Algorithm"):
  with members:
    - "Theory Revision in Fault Hierarchies"
    - "Boosting Trees for Cost-Sensitive Classifications"
    - "Cost-sensitive feature reduction applied to a hybrid genetic algorithm"
    - "Learning Active Classifiers"
    - "A systematic description of greedy optimisation algorithms for cost sensitive generalisation"
    - "Predicting Ordinal Classes in ILP"
    - "Evolution, Learning, and Instinct: 100 Years of the Baldwin Effect Using Learning to Facilitate the"

Connected through hyperedge HE-3 (induced by source paper "Appears in Working Notes, Integrating Multiple Learned Models for Improving and Scaling Machine Learning Algorithms"):
  with members:
    (none)

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