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: "Towards a Better Understanding of Memory-Based Reasoning Systems"
  Abstract: We quantify both experimentally and analytically the performance of memory-based reasoning (MBR) algorithms. To start gaining insight into the capabilities of MBR algorithms, we compare an MBR algorithm using a value difference metric to a popular Bayesian classifier. These two approaches are similar in that they both make certain independence assumptions about the data. However, whereas MBR uses specific cases to perform classification, Bayesian methods summarize the data probabilistically. We demonstrate that a particular MBR system called Pebls works comparatively well on a wide range of domains using both real and artificial data. With respect to the artificial data, we consider distributions where the concept classes are separated by functional discriminants, as well as time-series data generated by Markov models of varying complexity. Finally, we show formally that Pebls can learn (in the limit) natural concept classes that the Bayesian classifier cannot learn, and that it will attain perfect accuracy whenever

Connected through hyperedge HE-1 (induced by source paper "Logarithmic-Time Updates and Queries in Probabilistic Networks"):
  with members:
    - "Logarithmic Time Parallel Bayesian Inference"

Connected through hyperedge HE-2 (induced by source paper "Average-Case Analysis of a Nearest Neighbor Algorithm"):
  with members:
    - "Oblivious Decision Trees and Abstract Cases"
    - "Induction of One-Level Decision Trees"
    - "An Analysis of Bayesian Classifiers (1988), involves the formulation of average-case models for specific algorithms"
    - "Inductive Bias in Case-Based Reasoning Systems"
    - "PAC Analyses of a `Similarity Learning' IBL Algorithm"

Connected through hyperedge HE-3 (induced by source paper "EXPLORING A FRAMEWORK FOR INSTANCE BASED LEARNING AND NAIVE BAYESIAN CLASSIFIERS"):
  with members:
    - "Learning to Represent Codons: A Challenge Problem for Constructive Induction"
    - "A Weighted Nearest Neighbor Algorithm for Learning with Symbolic Features"

Connected through hyperedge HE-4 (induced by source paper "An Analysis of Bayesian Classifiers (1988), involves the formulation of average-case models for specific algorithms"):
  with members:
    - "Induction of One-Level Decision Trees"
    - "Computational Learning in Humans and Machines"
    - "Average-Case Analysis of a Nearest Neighbor Algorithm"
    - "A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection"
    - "Mining and Model Simplicity: A Case Study in Diagnosis"
    - "Issues in the Integration of Data Mining and Data Visualization Visualizing the Simple Bayesian Classifier"

Connected through hyperedge HE-5 (induced by source paper "A Weighted Nearest Neighbor Algorithm for Learning with Symbolic Features"):
  with members:
    - "Protein Secondary Structure Modelling with Probabilistic Networks (Extended Abstract)"
    - "Dynamical Selection of Learning Algorithms"
    - "A Comparative Study of ID3 and Backpropagation for English Text-to-Speech Mapping"
    - "Error-Based and Entropy-Based Discretization of Continuous Features"
    - "The Utility of Feature Weighting in Nearest-Neighbor Algorithms"
    - "Fast NP Chunking Using Memory-Based Learning Techniques"
    - "Resolving PP attachment Ambiguities with Memory-Based Learning"
    - "Memory-Based Lexical Acquisition and Processing"

Connected through hyperedge HE-6 (induced by source paper "Using Dirichlet Mixture Priors to Derive Hidden Markov Models for Protein Families"):
  with members:
    - "The megaprior heuristic for discovering protein sequence patterns"
    - "Minimum-Risk Profiles of Protein Families Based on Statistical Decision Theory"
    - "Homology Detection via Family Pairwise Search a straightforward generalization of pairwise sequence comparison algorithms to"
    - "Hidden Markov Models in Computational Biology: Applications to Protein Modeling UCSC-CRL-93-32 Keywords: Hidden Markov Models,"
    - "Optimal Alignments in Linear Space using Automaton-derived Cost Functions (Extended Abstract) Submitted to CPM'96"
    - "Finding Genes in DNA with a Hidden Markov Model"
    - "Dirichlet Mixtures: A Method for Improving Detection of Weak but Significant Protein Sequence Homology"
    - "Protein Secondary Structure Modelling with Probabilistic Networks (Extended Abstract)"

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: Case_Based