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: "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.

Connected through hyperedge HE-1 (induced by source paper "A Patient-Adaptive Neural Network ECG Patient Monitoring Algorithm"):
  with members:
    - "The Management of Context-Sensitive Features: A Review of Strategies"
    - "Synthesize, Optimize, Analyze, Repeat (SOAR): Application of Neural Network Tools to ECG Patient Monitoring"

Connected through hyperedge HE-2 (induced by source paper "Induction of Selective Bayesian Classifiers"):
  with members:
    - "Learning NonLinearly Separable Boolean Functions With Linear Threshold Unit Trees and Madaline-Style Networks"
    - "Searching for dependencies in Bayesian classifiers j A n V n j If the attributes"
    - "Learning Bayesian Prototype Trees by Simulated Annealing"
    - "Learning in neural networks with Bayesian prototypes"
    - "MDL Learning of Probabilistic Neural Networks for Discrete Problem Domains"
    - "Efficient Learning of Selective Bayesian Network Classifiers"

Connected through hyperedge HE-3 (induced by source paper "Learning One More Thing"):
  with members:
    - "The Functional Transfer of Knowledge for Coronary Artery Disease Diagnosis"
    - "Incremental Class Learning approach and its application to Handwritten Digit Recognition"
    - "The Canonical Distortion Measure for Vector Quantization and Function Approximation"
    - "Learning Model Bias minimum number of examples requred to learn a single task, and O(a"

Connected through hyperedge HE-4 (induced by source paper "Context-sensitive attribute estimation in regression"):
  with members:
    - "Employing Linear Regression in Regression Tree Leaves"
    - "Estimating Attributes: Analysis and Extensions of RELIEF"
    - "Context-Sensitive Feature Selection for Lazy Learners"
    - "Prognosing the Survival Time of the Patients with the Anaplastic Thyroid Carcinoma with Machine Learning"
    - "Overcoming the myopia of inductive learning algorithms with RELIEFF"

Connected through hyperedge HE-5 (induced by source paper "The Management of Context-Sensitive Features: A Review of Strategies"):
  with members:
    - "Context-Sensitive Feature Selection for Lazy Learners"
    - "A Patient-Adaptive Neural Network ECG Patient Monitoring Algorithm"
    - "Concept Learning and Flexible Weighting"

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: Theory