human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups papers that share a common citing paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "Recognition and Exploitation of Contextual Clues via Incremental Meta-Learning (Extended Version)"):
  Abstract: Daily experience shows that in the real world, the meaning of many concepts heavily depends on some implicit context, and changes in that context can cause more or less radical changes in the concepts. Incremental concept learning in such domains requires the ability to recognize and adapt to such changes. This paper presents a solution for incremental learning tasks where the domain provides explicit clues as to the current context (e.g., attributes with characteristic values). We present a general two-level learning model, and its realization in a system named MetaL(B), that can learn to detect certain types of contextual clues, and can react accordingly when a context change is suspected. The model consists of a base level learner that performs the regular on-line learning and classification task, and a meta-learner that identifies potential contextual clues. Context learning and detection occur during regular on-line learning, without separate training phases for context recognition. Experiments with synthetic domains as well as a `real-world' problem show that MetaL(B) is robust in a variety of dimensions and produces substantial improvement over simple object-level learning in situations with changing contexts.

Includes members:
  Member "Context-sensitive attribute estimation in regression":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Overcoming the myopia of inductive learning algorithms with RELIEFF")
  Member "Induction of Selective Bayesian Classifiers":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Searching for dependencies in Bayesian classifiers j A n V n j If the attributes")
  Member "The Management of Context-Sensitive Features: A Review of Strategies":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "A Patient-Adaptive Neural Network ECG Patient Monitoring Algorithm")
  Member "Learning One More Thing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "The Canonical Distortion Measure for Vector Quantization and Function Approximation")
  Member "A Patient-Adaptive Neural Network ECG Patient Monitoring Algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "The Management of Context-Sensitive Features: A Review of Strategies")

Question: Please tell me which class the citing paper (i.e., the source paper of the center hyperedge) 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: Theory