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: "ICML-96 Workshop "Learning in context-sensitive domains" Bari, Italy. Dynamically Adjusting Concepts to Accommodate Changing Contexts"
  Abstract: In concept learning, objects in a domain are grouped together based on similarity as determined by the attributes used to describe them. Existing concept learners require that this set of attributes be known in advance and presented in entirety before learning begins. Additionally, most systems do not possess mechanisms for altering the attribute set after concepts have been learned. Consequently, a veridical attribute set relevant to the task for which the concepts are to be used must be supplied at the onset of learning, and in turn, the usefulness of the concepts is limited to the task for which the attributes were originally selected. In order to efficiently accommodate changing contexts, a concept learner must be able to alter the set of descriptors without discarding its prior knowledge of the domain. We introduce the notion of attribute-incrementation, the dynamic modification of the attribute set used to describe instances in a problem domain. We have implemented the capability in a concept learning system that has been evaluated along several dimensions using an existing concept formation system for com parison.

Connected through hyperedge HE-1 (induced by source paper "Efficient Feature Selection in Conceptual Clustering"):
  with members:
    - "Irrelevant Features and the Subset Selection Problem"

Connected through hyperedge HE-2 (induced by source paper "Context-Sensitive Feature Selection for Lazy Learners"):
  with members:
    - "Learning to Refine Case Libraries:"
    - "Refining Conversational Case Libraries"
    - "An adaptation of Relief for attribute estimation in regression"
    - "Context-sensitive attribute estimation in regression"
    - "The Management of Context-Sensitive Features: A Review of Strategies"

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