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: "INFERENTIAL THEORY OF LEARNING: Developing Foundations for Multistrategy Learning"
  Abstract: The development of multistrategy learning systems should be based on a clear understanding of the roles and the applicability conditions of different learning strategies. To this end, this chapter introduces the Inferential Theory of Learning that provides a conceptual framework for explaining logical capabilities of learning strategies, i.e., their competence. Viewing learning as a process of modifying the learners knowledge by exploring the learners experience, the theory postulates that any such process can be described as a search in a knowledge space, triggered by the learners experience and guided by learning goals. The search operators are instantiations of knowledge transmutations, which are generic patterns of knowledge change. Transmutations may employ any basic type of inferencededuction, induction or analogy. Several fundamental knowledge transmutations are described in a novel and general way, such as generalization, abstraction, explanation and similization, and their counterparts, specialization, concretion, prediction and dissimilization, respectively. Generalization enlarges the reference set of a description (the set of entities that are being described). Abstraction reduces the amount of the detail about the reference set. Explanation generates premises that explain (or imply) the given properties of the reference set. Similization transfers knowledge from one reference set to a similar reference set. Using concepts of the theory, a multistrategy task-adaptive learning (MTL) methodology is outlined, and illustrated b y an example. MTL dynamically adapts strategies to the learning task, defined by the input information, learners background knowledge, and the learning goal. It aims at synergistically integrating a whole range of inferential learning strategies, such as empirical generalization, constructive induction, deductive generalization, explanation, prediction, abstraction, and similization.

Connected through hyperedge HE-1 (induced by source paper "Context-Based Similarity Applied to Retrieval of Relevant Cases"):
  with members:
    - "Constructive Similarity Assessment: Using Stored Cases to Define New Situations"
    - "MAC/FAC: A Model of Similarity-based Retrieval"
    - "How to Retrieve Relevant Information?"
    - "A Similarity-Based Retrieval Tool for Software Repositories"
    - "Applying Case-Based Reasoning to Control in Robotics"

Connected through hyperedge HE-2 (induced by source paper "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."):
  with members:
    - "Measuring the Difficulty of Specific Learning Problems"
    - "Trading Spaces: Computation, Representation and the Limits of Uninformed Learning"
    - "Discovering Complex Othello Strategies Through Evolutionary Neural Networks"
    - "USING MARKER-BASED GENETIC ENCODING OF NEURAL NETWORKS TO EVOLVE FINITE-STATE BEHAVIOUR"
    - "Evolving Obstacle Avoidance Behavior in a Robot Arm"
    - "A Comparison of Selection Schemes used in Genetic Algorithms"
    - "Determining Successful Negotiation Strategies: An Evolutionary Approach"
    - "Genetic Programming and Redundancy"

Connected through hyperedge HE-3 (induced by source paper "Architecture for Iterative Learning of Recursive Definitions"):
  with members:
    - "Integrity Constraints in ILP using a Monte Carlo approach"
    - "Learning by Refining Algorithm Sketches"

Connected through hyperedge HE-4 (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"
    - "How to Get a Free Lunch: A Simple Cost Model for Machine Learning Applications"
    - "Learning to Integrate Multiple Knowledge Sources for Case-Based Reasoning"

Connected through hyperedge HE-5 (induced by source paper "The Use of Explicit Goals for Knowledge to Guide Inference and Learning"):
  with members:
    - "Opportunistic Reasoning: A Design Perspective"
    - "A theory of questions and question asking"
    - "A Functional Theory of Creative Reading"
    - "an Opportunistic Enterprise"
    - "A Comparative Utility Analysis of Case-Based Reasoning and Control-Rule Learning Systems"
    - "Case-Based Planning to Learn"
    - "A Goal-Based Approach to Intelligent Information Retrieval"

Connected through hyperedge HE-6 (induced by source paper "Case-Based Planning to Learn"):
  with members:
    - "The Use of Explicit Goals for Knowledge to Guide Inference and Learning"
    - "Combining Rules and Cases to Learn Case Adaptation"

Connected through hyperedge HE-7 (induced by source paper "Learning Recursion with Iterative Bootstrap Induction (Extended Abstract)"):
  with members:
    - "Learning by Refining Algorithm Sketches"

Connected through hyperedge HE-8 (induced by source paper "A theory of questions and question asking"):
  with members:
    - "Integrating Creativity and Reading: A Functional Approach"
    - "The Use of Explicit Goals for Knowledge to Guide Inference and Learning"
    - "Decision Models: A Theory of Volitional Explanation"
    - "Incremental Learning of Explanation Patterns and their Indices"
    - "Indexing, Elaboration and Refinement: Incremental Learning of Explanatory Cases"
    - "Abstract"
    - "A Functional Theory of Creative Reading"

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