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: "82 Lag-space estimation in time-series modelling keep track of cases where the estimation of P"
  Abstract: When m = 0 (no delays), we set A 0 (ffi) = f(j; k) ; j 6= kg, such that P m (*jffi) depends only on *. The estimated probabilities above become quite noisy when the number of elements in set A m and B m are small. For this reason, we estimate the standard deviation of P m (*jffi). Notice that this estimate is the empirical average of a binomial variable (either a given couple satisfied the conditions on ffi and *, or it does not). The standard deviation is then estimated easily by: Generally speaking, P m (*jffi) increases with * (laxer output test), and when ffi approaches 0 (stricter input condition). Let us now define by P m (*) the maximum over ffi of P m (*jffi): P m (*) = max ffi&gt;0 P m (*jffi). The dependability index is defined as: P 0 (*) represents how much data passes the continuity test when no input information is available. This dependability index measures how much of the remaining continuity information is associated with involving input i m . This index is then averaged over * with respect to the probability (1 P 0 (*)): m (*) (1 P 0 (*)) d* (4.8) It is clear that m (*), and therefore its average, should be positive quantities. Furthermore, if the system is deterministic, the dependability is zero after a certain number of inputs, so the sum of averages saturates. If the system is also noise-free, they sum up to 1. For any m greater than the embedding dimension: refers to results obtained using this method. 4.6 Statistical variable selection Statistical variable selection (or feature selection) encompasses a number of techniques aimed at choosing a relevant subset of input variables in a regression or a classification problem. As in the rest of this document, we will limit ourselves to considerations related to the regression problem, even though most methods discussed below apply to classification as well. Variable selection can be seen as a part of the data analysis problem: the selection (or discard) of a variable tells us about the relevance of the associated measurement to the modelled system. In a general setting, this is a purely combinatorial problem: given V possible variables, there is 2 V possible subsets (including the empty set and the full set) of these variables. Given a performance measure, such as prediction error, the only optimal scheme is to test all these subset and choose the one that gives the best performance. It is easy to see that such an extensive scheme is only viable when the number of variables is rather low. Identifying 2 V models when we have more than a few variables requires too much computation. A number of techniques have been devised to overcome this combinatorial limit. Some of them use an iterative, locally optimal technique to construct an estimate of the relevant subset in a number of steps. We will refer to them as stepwise selection methods, not to be con fused with stepwise regression, a subset of these methods that we will address below. In forward selection, we start with an empty set of variables. At each step, we select a candidate variable using a selection criteria, check whether this variable should be added to the set, and iterate until a given stop condition is reached. On the contrary, backward elimination methods start with the full set of all input variables. At each step, the least significant variable is selected according to a selection criteria. If this variable is irrelevant, it is removed and the process is iterated until a stop condition is reached. It is easy to devise examples where the inclusion of a variable causes a previously included variable to become irrelevant. It thus seems appropriate to consider running a backward elimination each time a new variable is added by forward selection. This combination of both ap proaches is known as stepwise regression in the linear regression con

Connected through hyperedge HE-1 (induced by source paper "Bias, variance and prediction error for classification rules"):
  with members:
    - "The out-of-bootstrap method for model averaging and selection"
    - "Parametric regression 1.1 Learning problem model f bw b w in turn is an estimator"
    - "Bias Plus Variance Decomposition for Zero-One Loss Functions"
    - "Cross-Validation and the Bootstrap: Estimating the Error Rate of a Prediction Rule"
    - "An Efficient Method To Estimate Bagging's Generalization Error"

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