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: "Vapnik-Chervonenkis entropy of the spherical perceptron"
  Abstract: Perceptron learning of randomly labeled patterns is analyzed using a Gibbs distribution on the set of realizable labelings of the patterns. The entropy of this distribution is an extension of the Vapnik-Chervonenkis (VC) entropy, reducing to it exactly in the limit of infinite temperature. The close relationship between the VC and Gardner entropies can be seen within the replica formalism. There has been recent progress towards understanding the relationship between the statistical physics and Vapnik-Chervonenkis (VC) approaches to learning theory[1, 2, 3, 4]. The two approaches can be unified in a statistical mechanics based on the VC entropy. This paper treats the case of learning randomly labeled patterns, or the capacity problem, and extends some of the results of previous work[5, 6] to finite temperature. As will be explained in a companion paper, this extension is important for treating the generalization problem, which occurs in the context of learning patterns labeled by a target rule. Our general framework is illustrated for the simple perceptron sgn(w x), which maps an N -dimensional real-valued input x to a 1-valued output. Given a sample X = (x 1 ; : : : ; x m ) of inputs, the weight vector w determines a labeling L = (l 1 ; : : : ; l m ) of the sample via l i = sgn(w x i ). The weight vector w defines a normal hyperplane that separates the positive from the negative examples. The training error of a labeling L with respect to a reference labeling L 0 is defined by 1 m X 1 l i l 0 and is just the fraction of different labels in the two labelings. We consider the case in which the reference labeling is chosen at random, and address the issue of

Connected through hyperedge HE-1 (induced by source paper "Rigorous Learning Curve Bounds from Statistical Mechanics"):
  with members:
    - "Statistical Tests for Comparing Supervised Classification Learning Algorithms"
    - "Self bounding learning algorithms"
    - "Markov Decision Processes in Large State Spaces"
    - "Learning Curve Bounds for Markov Decision Processes with Undiscounted Rewards"
    - "A General Lower Bound on the Number of Examples Needed for Learning"
    - "Where Genetic Algorithms Excel"
    - "An Experimental and Theoretical Comparison of Model Selection Methods on simple model selection problems, the"
    - "Algorithmic Stability and Sanity-Check Bounds for Leave-One-Out Cross-Validation"

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