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: "The Power of a Pebble: Exploring and Mapping Directed Graphs"
  Abstract: Exploring and mapping an unknown environment is a fundamental problem, which is studied in a variety of contexts. Many works have focused on finding efficient solutions to restricted versions of the problem. In this paper, we consider a model that makes very limited assumptions on the environment and solve the mapping problem in this general setting. We model the environment by an unknown directed graph G, and consider the problem of a robot exploring and mapping G. We do not assume that the vertices of G are labeled, and thus the robot has no hope of succeeding unless it is given some means of distinguishing between vertices. For this reason we provide the robot with a pebble a device that it can place on a vertex and use to identify the vertex later. In this paper we show: (1) If the robot knows an upper bound on the number of vertices then it can learn the graph efficiently with only one pebble. (2) If the robot does not know an upper bound on the number of vertices n, then fi(log log n) pebbles are both necessary and sufficient. In both cases our algorithms are deterministic.

Connected through hyperedge HE-1 (induced by source paper "Exactly Learning Automata with Small Cover Time"):
  with members:
    - "Efficient Algorithms for Learning to Play Repeated Games Against Computationally Bounded Adversaries"
    - "The Power of Team Exploration: Two Robots Can Learn Unlabeled Directed Graphs"

Connected through hyperedge HE-2 (induced by source paper "Efficient Algorithms for Learning to Play Repeated Games Against Computationally Bounded Adversaries"):
  with members:
    - "A Competitive Approach to Game Learning"
    - "Planning and Learning in an Adversarial Robotic Game"
    - "Exactly Learning Automata with Small Cover Time"
    - "Optimality and Domination in Repeated Games with Bounded Players"

Connected through hyperedge HE-3 (induced by source paper "The Power of Team Exploration: Two Robots Can Learn Unlabeled Directed Graphs"):
  with members:
    - "How to Use Expert Advice (Extended Abstract)"
    - "Learning From a Population of Hypotheses"
    - "Exactly Learning Automata with Small Cover Time"
    - "Efficient Learning of Typical Finite Automata from Random Walks (Extended Abstract)"

Connected through hyperedge HE-4 (induced by source paper "Efficient Learning of Typical Finite Automata from Random Walks (Extended Abstract)"):
  with members:
    - "Cryptographic Limitations on Learning Boolean Formulae and Finite Automata"
    - "Learning Probabilistic Automata with Variable Memory Length"
    - "The Power of Team Exploration: Two Robots Can Learn Unlabeled Directed Graphs"
    - "On Learning Bounded-Width Branching Programs"
    - "New Evidence Driven State Merging Algorithm"
    - "On the Learnability and Usage of Acyclic Probabilistic Finite Automata"

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: Theory