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: "On Decision-Theoretic Foundations for Defaults"
  Abstract: In recent years, considerable effort has gone into understanding default reasoning. Most of this effort concentrated on the question of entailment, i.e., what conclusions are warranted by a knowledge-base of defaults. Surprisingly, few works formally examine the general role of defaults. We argue that an examination of this role is necessary in order to understand defaults, and suggest a concrete role for defaults: Defaults simplify our decision-making process, allowing us to make fast, approximately optimal decisions by ignoring certain possible states. In order to formalize this approach, we examine decision making in the framework of decision theory. We use probability and utility to measure the impact of possible states on the decision-making process. We accept a default if it ignores states with small impact according to our measure. We motivate our choice of measures and show that the resulting formalization of defaults satisfies desired properties of defaults, namely cumulative reasoning. Finally, we compare our approach with Poole's decision-theoretic defaults, and show how both can be combined to form an attractive framework for reasoning about decisions. We make numerous assumptions each day: the car will start, the road will not be blocked, there will be heavy traffic at 5pm, etc. Many of these assumptions are defeasible; we are willing to retract them given sufficient evidence. Humans naturally state defaults and draw conclusions from default information. Hence, defaults seem to play an important part in common-sense reasoning. To use such statements, however, we need a formal understanding of what defaults represent and what conclusions they admit. The problem of default entailment|roughly, what conclusions we should draw from a knowledge-base of defaults|has attracted a great deal of attention. Many researchers attempt to find "context-free" patterns of default reasoning (e.g., [ Kraus et al., 1990 ] ). As this research shows, much can be done in this approach. We claim, however, that the utility of this approach is limited; to gain a better understanding of defaults, we need to understand in what situations we should be willing to state a default. Our main thesis is that an investigation of defaults should elaborate their role in the behavior of the reasoning agent. This role should allow us to examine when a default is appropriate in terms of its implications on the agent's overall performance. In this paper, we suggest a particular role for defaults and show how this role allows us to provide a semantics for defaults. Of course, we do not claim that this is the only role defaults can play. In many applications, the end result of reasoning is a choice of actions. Usually, this choice is not optimal; there is too much uncertainty about the state of the world and the effects of actions to allow for an examination of all possibilities. We suggest that one role of defaults lies in simplifying our decision-making process by stating assumptions that reduce the space of examined possibilities. More precisely, we suggest that a default ' ! is a license to ignore : situations when our knowledge amounts to '. One particular suggestion that can be understood in this light is *-semantics [ Pearl, 1989 ] . In *-semantics, we accept a default ' ! if given the knowledge ', the probability of : is very small. This small probability of the :' states gives us a license to ignore them. Although probability plays an important part in our decisions, we claim that we should also examine the utility of our actions. For example, while most people think that it is highly unlikely that they will die next year, they also believe that they should not accept this as a default assumption in the context of a decision as to whether or not to buy life insurance. In this context, the stakes are too high to ignore this outcome, even though it is unlikely. We suggest that the license to ignore a set should be given based on its impact on our decision. To paraphrase this view, we should accept Bird ! Fly if assuming that the bird flies cannot get us into too much trouble. To formalize our intuitions we examine decision-making in the framework of decision theory [ Luce and Raiffa, 1957 ] . Decision theory represents a decision problem using several components: a set of possible states, a probability measure over these sets, and a utility function that assigns to each action and state a numerical value. fl To appear in IJCAI'95.

Connected through hyperedge HE-1 (induced by source paper "Constructive Belief and Rational Representation"):
  with members:
    - "Rational Belief Revision (Preliminary Report)"
    - "Rationality and its Roles in Reasoning"
    - "Impediments to Universal Preference-Based Default Theories"

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: Probabilistic_Methods