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: "A Patient-Adaptive Neural Network ECG Patient Monitoring Algorithm"
  Abstract: The patient-adaptive classifier was compared with a well-established baseline algorithm on six major databases, consisting of over 3 million heartbeats. When trained on an initial 77 records and tested on an additional 382 records, the patient-adaptive algorithm was found to reduce the number of Vn errors on one channel by a factor of 5, and the number of Nv errors by a factor of 10. We conclude that patient adaptation provides a significant advance in classifying normal vs. ventricular beats for ECG Patient Monitoring.

Connected through hyperedge HE-1 (induced by source paper "Recognition and Exploitation of Contextual Clues via Incremental Meta-Learning (Extended Version)"):
  with members:
    - "Context-sensitive attribute estimation in regression"
    - "Induction of Selective Bayesian Classifiers"
    - "The Management of Context-Sensitive Features: A Review of Strategies"
    - "Learning One More Thing"

Connected through hyperedge HE-2 (induced by source paper "The Management of Context-Sensitive Features: A Review of Strategies"):
  with members:
    - "Context-Sensitive Feature Selection for Lazy Learners"
    - "Recognition and Exploitation of Contextual Clues via Incremental Meta-Learning (Extended Version)"
    - "Concept Learning and Flexible Weighting"

Connected through hyperedge HE-3 (induced by source paper "Synthesize, Optimize, Analyze, Repeat (SOAR): Application of Neural Network Tools to ECG Patient Monitoring"):
  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: Neural_Networks