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: "Book Review  Introduction to the Theory of Neural Computation Reviewed by: 2"
  Abstract: Neural computation, also called connectionism, parallel distributed processing, neural network modeling or brain-style computation, has grown rapidly in the last decade. Despite this explosion, and ultimately because of impressive applications, there has been a dire need for a concise introduction from a theoretical perspective, analyzing the strengths and weaknesses of connectionist approaches and establishing links to other disciplines, such as statistics or control theory. The Introduction to the Theory of Neural Computation by Hertz, Krogh and Palmer (subsequently referred to as HKP) is written from the perspective of physics, the home discipline of the authors. The book fulfills its mission as an introduction for neural network novices, provided that they have some background in calculus, linear algebra, and statistics. It covers a number of models that are often viewed as disjoint. Critical analyses and fruitful comparisons between these models

Connected through hyperedge HE-1 (induced by source paper "Induction of Multiscale Temporal Structure"):
  with members:
    - "Tau Net: A Neural Network for Modeling Temporal Variability"
    - "Neural net architectures for temporal sequence processing"
    - "REDUCED MEMORY REPRESENTATIONS FOR MUSIC"
    - "Learning Sequential Tasks by Incrementally Adding Higher Orders"
    - "A Connectionist Symbol Manipulator That Discovers the Structure of Context-Free Languages"
    - "Diffusion of Context and Credit Information in Markovian Models"
    - "Sequence Learning with Incremental Higher-Order Neural Networks"

Connected through hyperedge HE-2 (induced by source paper "On the Applicability of Neural Network and Machine Learning Methodologies to Natural Language Processing"):
  with members:
    - "Can Recurrent Neural Networks Learn Natural Language Grammars? W&Z recurrent neural networks are able to"
    - "Learning Feature-based Semantics with Simple Recurrent Networks"

Connected through hyperedge HE-3 (induced by source paper "Computational Models of Sensorimotor Integration  Computational Maps and Motor Control."):
  with members:
    - "Forward models: Supervised learning with a distal teacher"

Connected through hyperedge HE-4 (induced by source paper "Control Systems Magazine, 14, 1, pp.57-71. Robot Juggling: An Implementation of Memory-based Learning"):
  with members:
    - "Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming"
    - "In  Active Learning with Statistical Models"
    - "Forward models: Supervised learning with a distal teacher"
    - "Efficient Locally Weighted Polynomial Regression Predictions"
    - "Reinforcement Learning in the Multi-Robot Domain"
    - "Locally Weighted Learning for Control"
    - "Using Local Trajectory Optimizers To Speed Up Global Optimization In Dynamic Programming"

Connected through hyperedge HE-5 (induced by source paper "Chain graphs for learning"):
  with members:
    - "On the Markov Equivalence of Chain Graphs, Undirected Graphs, and Acyclic Digraphs"
    - "Operations for Learning with Graphical Models decomposition techniques and the demonstration that graphical models provide"

Connected through hyperedge HE-6 (induced by source paper "Backpropagation Convergence Via Deterministic Nonmonotone Perturbed Minimization"):
  with members:
    - "Mathematical Programming in Neural Networks"
    - "June 1994 T o app ear in Neural Computation A Coun terexample to T emp"
    - "Parallel Gradient Distribution in Unconstrained Optimization"

Connected through hyperedge HE-7 (induced by source paper "Disambiguation and Grammar as Emergent Soft Constraints"):
  with members:
    - "Subsymbolic Case-Role Analysis of Sentences with Embedded Clauses"

Connected through hyperedge HE-8 (induced by source paper "Learning to Predict Reading Frames in E. coli DNA Sequences"):
  with members:
    - "Investigating the Value of a Good Input Representation"
    - "Protein Structure Prediction: Selecting Salient Features from Large Candidate Pools"
    - "Learning to Represent Codons: A Challenge Problem for Constructive Induction"

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