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: "Context-Specific Independence in Bayesian Networks"
  Abstract: Bayesiannetworks provide a languagefor qualitatively representing the conditional independence properties of a distribution. This allows a natural and compact representation of the distribution, eases knowledge acquisition, and supports effective inference algorithms. It is well-known, however, that there are certain independencies that we cannot capture qualitatively within the Bayesian network structure: independencies that hold only in certain contexts, i.e., given a specific assignment of values to certain variables. In this paper, we propose a formal notion of context-specific independence (CSI), based on regularities in the conditional probability tables (CPTs) at a node. We present a technique, analogous to (and based on) d-separation, for determining when such independence holds in a given network. We then focus on a particular qualitative representation schemetree-structured CPTs for capturing CSI. We suggest ways in which this representation can be used to support effective inference algorithms. In particular, we present a structural decomposition of the resulting network which can improve the performance of clustering algorithms, and an alternative algorithm based on cutset conditioning.

Connected through hyperedge HE-1 (induced by source paper "The Frame Problem and Bayesian Network Action Representations"):
  with members:
    - "Structured Arc Reversal and Simulation of Dynamic Probabilistic Networks"
    - "Structured Reachability Analysis for Markov Decision Processes"

Connected through hyperedge HE-2 (induced by source paper "Exploiting Causal Independence in Bayesian Network Inference"):
  with members:
    - "BUCKET ELIMINATION: A UNIFYING FRAMEWORK FOR PROBABILISTIC INFERENCE"
    - "Robustness Analysis of Bayesian Networks with Global Neighborhoods"
    - "Robustness Analysis of Bayesian Networks with Finitely Generated Convex Sets of Distributions"
    - "Incremental Tradeoff Resolution in Qualitative Probabilistic Networks"

Connected through hyperedge HE-3 (induced by source paper "BUCKET ELIMINATION: A UNIFYING FRAMEWORK FOR PROBABILISTIC INFERENCE"):
  with members:
    - "Exploiting Causal Independence in Bayesian Network Inference"
    - "Robustness Analysis of Bayesian Networks with Global Neighborhoods"
    - "Robustness Analysis of Bayesian Networks with Finitely Generated Convex Sets of Distributions"
    - "Topological Parameters for time-space tradeoff"
    - "Global Conditioning for Probabilistic Inference in Belief Networks"

Connected through hyperedge HE-4 (induced by source paper "LEARNING BAYESIAN NETWORKS WITH LOCAL STRUCTURE"):
  with members:
    - "Structured Arc Reversal and Simulation of Dynamic Probabilistic Networks"
    - "Sequential Update of Bayesian Network Structure"
    - "A Tutorial on Learning With Bayesian Networks"
    - "On the Sample Complexity of Learning Bayesian Networks"

Connected through hyperedge HE-5 (induced by source paper "Structured Representation of Complex Stochastic Systems"):
  with members:
    - "Global Conditioning for Probabilistic Inference in Belief Networks"
    - "Factorial Hidden Markov Models"
    - "Stochastic simulation algorithms for dynamic probabilistic networks"
    - "Tractable Inference for Complex Stochastic Processes"

Connected through hyperedge HE-6 (induced by source paper "Structured Arc Reversal and Simulation of Dynamic Probabilistic Networks"):
  with members:
    - "The Frame Problem and Bayesian Network Action Representations"
    - "LEARNING BAYESIAN NETWORKS WITH LOCAL STRUCTURE"
    - "Dynamic Belief Networks for Discrete Monitoring"
    - "Stochastic simulation algorithms for dynamic probabilistic networks"

Connected through hyperedge HE-7 (induced by source paper "A Constraint-Based Approach to Preference Elicitation and Decision Making"):
  with members:
    - "Utility Elicitation as a Classification Problem"

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