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: "Learning to Predict User Operations for Adaptive Scheduling"
  Abstract: Mixed-initiative systems present the challenge of finding an effective level of interaction between humans and computers. Machine learning presents a promising approach to this problem in the form of systems that automatically adapt their behavior to accommodate different users. In this paper, we present an empirical study of learning user models in an adaptive assistant for crisis scheduling. We describe the problem domain and the scheduling assistant, then present an initial formulation of the adaptive assistant's learning task and the results of a baseline study. After this, we report the results of three subsequent experiments that investigate the effects of problem reformulation and representation augmentation. The results suggest that problem reformulation leads to significantly better accuracy without sacrificing the usefulness of the learned behavior. The studies also raise several interesting issues in adaptive assistance for scheduling.

Connected through hyperedge HE-1 (induced by source paper "A Reinforcement Learning Approach to Job-shop Scheduling"):
  with members:
    - "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,"
    - "High-Performance Job-Shop Scheduling With A Time-Delay TD() Network"
    - "Generalization in Reinforcement Learning: Safely Approximating the Value Function"
    - "Finding Structure in Reinforcement Learning"
    - "Value Function Based Production Scheduling"
    - "Value Function Approximations and Job-Shop Scheduling"
    - "Solving Combinatorial Optimization Tasks by Reinforcement Learning: A General Methodology Applied to Resource-Constrained Scheduling"
    - "Robust Value Function Approximation by Working Backwards Computing an accurate value function is the key"

Connected through hyperedge HE-2 (induced by source paper "on Case-Based Reasoning Integrations Case-Based Seeding for an Interactive Crisis Response Assistant"):
  with members:
    - "Acquiring Case Adaptation Knowledge: A Hybrid Approach"

Connected through hyperedge HE-3 (induced by source paper "Evaluating Computational Assistance for Crisis Response"):
  with members:
    - "Combining Rules and Cases to Learn Case Adaptation"

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