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: "Predicting Conditional Probability Distributions: A Connectionist Approach"
  Abstract: Most traditional prediction techniques deliver the mean of the probability distribution (a single point). For multimodal processes, instead of predicting the mean of the probability distribution, it is important to predict the full distribution. This article presents a new connectionist method to predict the conditional probability distribution in response to an input. The main idea is to transform the problem from a regression to a classification problem. The conditional probability distribution network can perform both direct predictions and iterated predictions, a task which is specific for time series problems. We compare our method to fuzzy logic and discuss important differences, and also demonstrate the architecture on two time series. The first is the benchmark laser series used in the Santa Fe competition, a deterministic chaotic system. The second is a time series from a Markov process which exhibits structure on two time scales. The network produces multimodal predictions for this series. We compare the predictions of the network with a nearest-neighbor predictor and find that the conditional probability network is more than twice as likely a model.

Connected through hyperedge HE-1 (induced by source paper "``Learning Local Error Bars for Nonlinear Regression.''  Learning Local Error Bars for Nonlinear Regression"):
  with members:
    - "Avoiding overfitting by locally matching the noise level of the data gating network discovers the"
    - "Direct Multi-Step Time Series Prediction Using TD()"
    - "NONLINEAR TRADING MODELS THROUGH SHARPE RATIO MAXIMIZATION"
    - "Evaluating Neural Network Predictors by Bootstrapping"
    - "Predictions with Confidence Intervals (Local Error Bars)"

Connected through hyperedge HE-2 (induced by source paper "The Observer-Observation Dilemma in Neuro-Forecasting: Reliable Models From Unreliable Data Through CLEARNING"):
  with members:
    - "Selecting Input Variables Using Mutual Information and Nonparametric Density Estimation"
    - "Evaluating Neural Network Predictors by Bootstrapping"
    - "Predictions with Confidence Intervals (Local Error Bars)"

Connected through hyperedge HE-3 (induced by source paper "Avoiding overfitting by locally matching the noise level of the data gating network discovers the"):
  with members:
    - "Hierarchical Mixtures of Experts and the EM Algorithm"
    - "Forecasting electricity demand using nonlinear mixture of experts"
    - "Predictions with Confidence Intervals (Local Error Bars)"
    - "``Learning Local Error Bars for Nonlinear Regression.''  Learning Local Error Bars for Nonlinear Regression"

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