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: "The Observer-Observation Dilemma in Neuro-Forecasting: Reliable Models From Unreliable Data Through CLEARNING"
  Abstract: This paper introduces the idea of clearning, of simultaneously cleaning data and learning the underlying structure. The cleaning step can be viewed as top-down processing (the model modifies the data), and the learning step can be viewed as bottom-up processing (where the data modifies the model). After discussing the statistical foundation of the proposed method from a maximum likelihood perspective, we apply clearning to a notoriously hard problem where benchmark performances are very well known: the prediction of foreign exchange rates. On the difficult 1993-1994 test period, clearning in conjunction with pruning yields an annualized return between 35 and 40% (out-of-sample), significantly better than an otherwise identical network trained without cleaning. The network was started with 69 inputs and 15 hidden units and ended up with only 39 non-zero weights between inputs and hidden units. The resulting ultra-sparse final architectures obtained with clearning and pruning are immune against overfitting, even on very noisy problems since the cleaned data allow for a simpler model. Apart from the very competitive performance, clearning gives insight into the data: we show how to estimate the overall signal-to-noise ratio of each input variable, and we show that error estimates for each pattern can be used to detect and remove outliers, and to replace missing or corrupted data by cleaned values. Clearning can be used in any nonlinear regression or classification problem.

Connected through hyperedge HE-1 (induced by source paper "Predictions with Confidence Intervals (Local Error Bars)"):
  with members:
    - "Avoiding overfitting by locally matching the noise level of the data gating network discovers the"
    - "A comparison of some error estimates for neural network models  Summary"
    - "Evaluating Neural Network Predictors by Bootstrapping"
    - "``Learning Local Error Bars for Nonlinear Regression.''  Learning Local Error Bars for Nonlinear Regression"

Connected through hyperedge HE-2 (induced by source paper "Selecting Input Variables Using Mutual Information and Nonparametric Density Estimation"):
  with members:
    - "A Practical Bayesian Framework for Backprop Networks"
    - "Hoeffding Races: Accelerating Model Selection Search for Classification and Function Approximation"

Connected through hyperedge HE-3 (induced by source paper "Evaluating Neural Network Predictors by Bootstrapping"):
  with members:
    - "A comparison of some error estimates for neural network models  Summary"
    - "Predictions with Confidence Intervals (Local Error Bars)"
    - "``Learning Local Error Bars for Nonlinear Regression.''  Learning Local Error Bars for Nonlinear Regression"

Connected through hyperedge HE-4 (induced by source paper "Predicting Conditional Probability Distributions: A Connectionist Approach"):
  with members:
    - "Avoiding overfitting by locally matching the noise level of the data gating network discovers the"
    - "``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