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: "Rationality and Intelligence"
  Abstract: The long-term goal of our field is the creation and understanding of intelligence. Productive research in AI, both practical and theoretical, benefits from a notion of intelligence that is precise enough to allow the cumulative development of robust systems and general results. The concept of rational agency has long been considered a leading candidate to fulfill this role. This paper outlines a gradual evolution in the formal conception of rationality that brings it closer to our informal conception of intelligence and simultaneously reduces the gap between theory and practice. Some directions for future research are indicated.

Connected through hyperedge HE-1 (induced by source paper "Approximating Optimal Policies for Partially Observable Stochastic Domains"):
  with members:
    - "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,"
    - "Learning policies for partially observable environments: Scaling up"
    - "Acting under Uncertainty: Discrete Bayesian Models for Mobile-Robot Navigation"
    - "Incremental methods for computing bounds in partially observable Markov decision processes"
    - "Efficient dynamic-programming updates in partially observable Markov decision processes"
    - "Reinforcement Learning Algorithm for Partially Observable Markov Decision Problems"
    - "Adaptive probabilistic networks"

Connected through hyperedge HE-2 (induced by source paper "A Formalization of Explanation-Based Macro-operator Learning"):
  with members:
    - "Quantifying Prior Determination Knowledge using the PAC Learning Model"
    - "A Theory of Unsupervised Speedup Learning"

Connected through hyperedge HE-3 (induced by source paper "The BATmobile: Towards a Bayesian Automated Taxi"):
  with members:
    - "Adaptive probabilistic networks"
    - "Stochastic simulation algorithms for dynamic probabilistic networks"
    - "Tractable Inference for Complex Stochastic Processes"
    - "Space-efficient inference in dynamic probabilistic networks"
    - "Accounting for Context in Plan Recognition, with Application to Traffic Monitoring"
    - "Fall Diagnosis using Dynamic Belief Networks"

Connected through hyperedge HE-4 (induced by source paper "Toward Efficient Agnostic Learning"):
  with members:
    - "How to Use Expert Advice (Extended Abstract)"
    - "Learning in the Presence of Malicious Errors"
    - "Cryptographic Limitations on Learning Boolean Formulae and Finite Automata"
    - "Prediction, Learning, Uniform Convergence, and Scale-sensitive Dimensions"
    - "Tracking Drifting Concepts By Minimizing Disagreements"
    - "On Learning Conjunctions with Malicious Noise"
    - "Learning Switching Concepts"
    - "Efficient Distribution-free Learning of Probabilistic Concepts"

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: Probabilistic_Methods