human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups papers that share a common citing paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "Avoiding overfitting by locally matching the noise level of the data gating network discovers the":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Forecasting electricity demand using nonlinear mixture of experts")
  Member "The Observer-Observation Dilemma in Neuro-Forecasting: Reliable Models From Unreliable Data Through CLEARNING":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Predictions with Confidence Intervals (Local Error Bars)")
  Member "``Learning Local Error Bars for Nonlinear Regression.''  Learning Local Error Bars for Nonlinear Regression":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Predictions with Confidence Intervals (Local Error Bars)")

Question: Please tell me which class the citing paper (i.e., the source paper of the center hyperedge) 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