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: "BACKPROPAGATION SEPARATES WHERE PERCEPTRONS DO"
  Abstract: Feedforward nets with sigmoidal activation functions are often designed by minimizing a cost criterion. It has been pointed out before that this technique may be outperformed by the classical perceptron learning rule, at least on some problems. In this paper, we show that no such pathologies can arise if the error criterion is of a threshold LMS type, i.e., is zero for values "beyond" the desired target values. More precisely, we show that if the data are linearly separable, and one considers nets with no hidden neurons, then an error function as above cannot have any local minima that are not global. Simulations of networks with hidden units are consistent with these results, in that often data which can be classified when minimizing a threshold LMS criterion may fail to be classified when using instead a simple LMS cost. In addition, the proof gives the following stronger result, under the stated hypotheses: the continuous gradient adjustment procedure is such that from any initial weight configuration a separating set of weights is obtained in finite time. This is a precise analogue of the Perceptron Learning Theorem. The results are then compared with the more classical pattern recognition problem of threshold LMS with linear activations, where no spurious local minima exist even for nonseparable data: here it is shown that even if using the threshold criterion, such bad local minima may occur, if the data are not separable and sigmoids are used.

Connected through hyperedge HE-1 (induced by source paper "Exponentially many local minima for single neurons"):
  with members:
    - "BACKPROPAGATION CAN GIVE RISE TO SPURIOUS LOCAL MINIMA EVEN FOR NETWORKS WITHOUT HIDDEN LAYERS"
    - "On the Distribution of Performance from Multiple Neural Network Trials, On the Distribution of Performance"
    - "Relative Loss Bounds for Multidimensional Regression Problems"

Connected through hyperedge HE-2 (induced by source paper "LINEAR SYSTEMS WITH SIGN-OBSERVATIONS"):
  with members:
    - "Sample Complexity for Learning Recurrent Perceptron Mappings"
    - "Interconnected Automata and Linear Systems: A Theoretical Framework in Discrete-Time In Hybrid Systems III: Verification"
    - "Lemma 2.3 The system is reachable and observable and realizes the same input/output behavior as"
    - "Observability of Linear Systems with Saturated Outputs"

Connected through hyperedge HE-3 (induced by source paper "BACKPROPAGATION CAN GIVE RISE TO SPURIOUS LOCAL MINIMA EVEN FOR NETWORKS WITHOUT HIDDEN LAYERS"):
  with members:
    - "Critical Points for Least-Squares Problems Involving Certain Analytic Functions, with Applications to Sigmoidal Nets"
    - "Exponentially many local minima for single neurons"

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