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: "Proben1 A Set of Neural Network Benchmark Problems and Benchmarking Rules"
  Abstract: Proben1 is a collection of problems for neural network learning in the realm of pattern classification and function approximation plus a set of rules and conventions for carrying out benchmark tests with these or similar problems. Proben1 contains 15 data sets from 12 different domains. All datasets represent realistic problems which could be called diagnosis tasks and all but one consist of real world data. The datasets are all presented in the same simple format, using an attribute representation that can directly be used for neural network training. Along with the datasets, Proben1 defines a set of rules for how to conduct and how to document neural network benchmarking. The purpose of the problem and rule collection is to give researchers easy access to data for the evaluation of their algorithms and networks and to make direct comparison of the published results feasible. This report describes the datasets and the benchmarking rules. It also gives some basic performance measures indicating the difficulty of the various problems. These measures can be used as baselines for comparison.

Connected through hyperedge HE-1 (induced by source paper "Comparing Adaptive and Non-Adaptive Connection Pruning With Pure Early Stopping"):
  with members:
    - "A Quantitative Study of Experimental Evaluations of Neural Network Learning Algorithms: Current Research Practice"

Connected through hyperedge HE-2 (induced by source paper "An Evolutionary Method to Find Good Building-Blocks for Architectures of Artificial Neural Networks"):
  with members:
    - "A Comparison between Cellular Encoding and Direct Encoding for Genetic Neural Networks"

Connected through hyperedge HE-3 (induced by source paper "Connection Pruning with Static and Adaptive Pruning Schedules"):
  with members:
    - "A Quantitative Study of Experimental Evaluations of Neural Network Learning Algorithms: Current Research Practice"
    - "CuPit-2: Portable and Efficient High-Level Parallel Programming of Neural Networks for the Systems Analysis Modelling"

Connected through hyperedge HE-4 (induced by source paper "CuPit-2: Portable and Efficient High-Level Parallel Programming of Neural Networks for the Systems Analysis Modelling"):
  with members:
    - "A Parallel Programming Model for Irregular Dynamic Neural Networks a programming model that allows to"
    - "Connection Pruning with Static and Adaptive Pruning Schedules"
    - "CuPit-2 A Parallel Language for Neural Algorithms: Language Reference and Tutorial"

Connected through hyperedge HE-5 (induced by source paper "A Parallel Programming Model for Irregular Dynamic Neural Networks a programming model that allows to"):
  with members:
    - "Adaptive Parameter Pruning in Neural Networks"
    - "CuPit-2: Portable and Efficient High-Level Parallel Programming of Neural Networks for the Systems Analysis Modelling"
    - "CuPit-2 A Parallel Language for Neural Algorithms: Language Reference and Tutorial"

Connected through hyperedge HE-6 (induced by source paper "Interpretable Neural Networks with BP-SOM"):
  with members:
    - "Measuring the Difficulty of Specific Learning Problems"
    - "Cholinergic suppression of transmission may allow combined associative memory function and self-organization in the neocortex."
    - "Avoiding Overfitting with BP-SOM"

Connected through hyperedge HE-7 (induced by source paper "A Quantitative Study of Experimental Evaluations of Neural Network Learning Algorithms: Current Research Practice"):
  with members:
    - "Comparison of Bayesian and Neural Net Unsupervised Classification Techniques"
    - "Comparing Adaptive and Non-Adaptive Connection Pruning With Pure Early Stopping"
    - "Adaptive Parameter Pruning in Neural Networks"
    - "Connection Pruning with Static and Adaptive Pruning Schedules"
    - "Averaging and Data Snooping"
    - "Statistical Evaluation of Neural Network Experiments: Minimum Requirements and Current Practice"

Connected through hyperedge HE-8 (induced by source paper "Error-Correcting Output Codes: A General Method for Improving Multiclass Inductive Learning Programs"):
  with members:
    - "Learning the Past Tense of English Verbs: The Symbolic Pattern Associator vs. Connectionist Models"
    - "Error-Correcting Output Coding Corrects Bias and Variance"
    - "A Comparative Study of ID3 and Backpropagation for English Text-to-Speech Mapping"
    - "LEARNING BY USING DYNAMIC FEATURE COMBINATION AND SELECTION"
    - "Machine Learning Bias, Statistical Bias, and Statistical Variance of Decision Tree Algorithms"
    - "Language-Independent Data-Oriented Grapheme-to-Phoneme Conversion"
    - "Error-Correcting Output Codes for Local Learners"
    - "Probability Estimation via Error-Correcting Output Coding"

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