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: "Using Precepts to Augment Training Set Learning an input whose value is don't-care in some"
  Abstract: are used in turn to approximate A. Empirical studies show that good results can be achieved with TSL [8, 11]. However, TSL has several drawbacks. Training set learners (e.g., backpropagation) are typically slow as they may require many passes over the training set. Also, there is no guarantee that, given an arbitrary training set, the system will find enough good critical features to get a reasonable approximation of A. Moreover, the number of features to be searched is exponential in the number of inputs, and TSL becomes computationally expensive [1]. Finally, the scarcity of interesting positive theoretical results suggests the difficulty of learning without sufficient a priori knowledge. The goal of learning systems is to generalize. Generalization is commonly based on the set of critical features the system has available. Training set learners typically extract critical features from a random set of examples. While this approach is attractive, it suffers from the exponential growth of the number of features to be searched. We propose to extend it by endowing the system with some a priori knowledge, in the form of precepts. Advantages of the augmented system are speedup, improved generalization, and greater parsimony. This paper presents a precept-driven learning algorithm. Its main features include: 1) distributed implementation, 2) bounded learning and execution times, and 3) ability to handle both correct and incorrect precepts. Results of simulations on real-world data demonstrate promise. This paper presents precept-driven learning (PDL). PDL is intended to overcome some of TSL's weaknesses. In PDL, the training set is augmented by a small set of precepts. A pair p = (i, o) in I O is called an example. A precept is an example in which some of the i-entries (inputs) are set to the special value don't-care. An input whose value is not don't-care is said to be asserted. If i has no effect on the value of the output. The use of the special value don't-care is therefore as a shorthand. A pair containing don't-care inputs represents as many examples as the product of the sizes of the input domains of its don't-care inputs. 1. Introduction

Connected through hyperedge HE-1 (induced by source paper "Beyond predictive accuracy: what?"):
  with members:
    - "Model selection using measure functions"

Connected through hyperedge HE-2 (induced by source paper "AN INCREMENTAL LEARNING MODEL FOR COMMONSENSE REASONING"):
  with members:
    (none)

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